{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import csv\n",
    "import re\n",
    "import os\n",
    "import polars as pl\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import utils\n",
    "\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from matplotlib.axes import Axes\n",
    "\n",
    "def parse_trace(trace_file_name: str) -> pl.DataFrame:\n",
    "    \"\"\"\n",
    "    # parse_trace\n",
    "\n",
    "    Parse a trace file and return a polars DataFrame.\n",
    "\n",
    "        Parameters:\n",
    "            trace_file_name (str): The name of the trace file.\n",
    "\n",
    "        Returns:\n",
    "            polars.DataFrame: The parsed trace.\n",
    "\n",
    "    Format of the trace file:\n",
    "        (line number as index) gap ddl size prio\n",
    "\n",
    "        Example:\n",
    "            ```\n",
    "            0.1 200 1300 1\n",
    "            0.1 200 1300 2\n",
    "    \"\"\"\n",
    "    trace = []\n",
    "    with open(trace_file_name, \"r\") as f:\n",
    "        lines = f.readlines()\n",
    "        for idx, line in enumerate(lines):\n",
    "            info = re.split(r\"\\s+\", line)\n",
    "            trace.append(\n",
    "                {\n",
    "                    \"id\": idx,\n",
    "                    \"start\": float(info[0]),\n",
    "                    \"gap\": float(info[0]),\n",
    "                    \"ddl\": int(info[1]),\n",
    "                    \"size\": int(info[2]),\n",
    "                    \"prio\": int(info[3]),\n",
    "                }\n",
    "            )\n",
    "    trace = pl.from_dicts(trace)\n",
    "    trace[\"start\"] = trace[\"start\"].cumsum()\n",
    "    return trace\n",
    "\n",
    "def parse_server_log(server_log_file_name: str) -> pl.DataFrame:\n",
    "    \"\"\"\n",
    "    # parse_server_log\n",
    "\n",
    "    Parse a server log file and return a polars DataFrame.\n",
    "\n",
    "        Parameters:\n",
    "            server_log_file_name (str): The name of the server log file.\n",
    "\n",
    "        Returns:\n",
    "            polars.DataFrame: The parsed server log.\n",
    "\n",
    "    Format of the server log file:\n",
    "        CSV file with following columns:\n",
    "        - block_id\n",
    "        - start\n",
    "        - complete\n",
    "        - cancelled\n",
    "        - cancelled_passed\n",
    "    \"\"\"\n",
    "    try:\n",
    "        server_log = pl.read_csv(server_log_file_name)\n",
    "        server_log[\"block_id\"] = server_log[\"block_id\"].apply(lambda x: (x >> 2) - 1)\n",
    "        return server_log\n",
    "    except:\n",
    "        return pl.DataFrame(\n",
    "            None, [\"block_id\", \"start\", \"complete\", \"cancelled\", \"cancelled_passed\"]\n",
    "        )\n",
    "\n",
    "def parse_result(result_file_name: str) -> pl.DataFrame:\n",
    "    \"\"\"\n",
    "    # parse_result\n",
    "\n",
    "    Parse a result file and return a polars DataFrame.\n",
    "\n",
    "        Parameters:\n",
    "            result_file_name (str): The name of the result file.\n",
    "\n",
    "        Returns:\n",
    "            polars.DataFrame: The parsed result.\n",
    "\n",
    "    Format of the result file:\n",
    "        CSV file with following columns:\n",
    "        - block_id\n",
    "        - bct\n",
    "        - size\n",
    "        - priority\n",
    "        - deadline\n",
    "        - duration\n",
    "    \"\"\"\n",
    "    try:\n",
    "        result = pl.read_csv(result_file_name)\n",
    "        result[\"block_id\"] = result[\"block_id\"].apply(lambda x: (x >> 2) - 1)\n",
    "        return result\n",
    "    except:\n",
    "        return pl.DataFrame(\n",
    "            None, [\"block_id\", \"bct\", \"size\", \"priority\", \"deadline\", \"duration\"]\n",
    "        )\n",
    "        \n",
    "def find_unsend(result_file_name, trace_file_name):\n",
    "    if trace_file_name is not None:\n",
    "        block_num = utils.count_newlines(trace_file_name)\n",
    "        trace = set(range(block_num))\n",
    "\n",
    "        with open(result_file_name, \"r\") as f:\n",
    "            reader = csv.DictReader(f)\n",
    "            result = [((int(row[\"block_id\"]) - 1) >> 2) - 1 for row in reader]\n",
    "\n",
    "        res = sorted([(x, ((x + 1) << 2) + 1) for x in trace - set(result)])\n",
    "\n",
    "        return (len(res), res)\n",
    "    else:\n",
    "        raise Exception(\"trace_file_name is None\")\n",
    "\n",
    "\n",
    "def total_time(trace_file_name):\n",
    "    with open(trace_file_name, \"r\") as f:\n",
    "        reader = csv.reader(f, delimiter=\" \")\n",
    "        return sum([float(row[0]) for row in reader])\n",
    "\n",
    "\n",
    "def draw(result_file_name, trace_file_name):\n",
    "    result = pl.read_csv(result_file_name)\n",
    "    result[\"block_id\"] = result[\"block_id\"].apply(lambda x: (x >> 2) - 1)\n",
    "    trace = parse_trace(trace_file_name).rename({\"gap\": \"start\"})\n",
    "    trace[\"start\"] = trace[\"start\"].cumsum()\n",
    "    result = result.join(trace, left_on=\"block_id\", right_on=\"id\", how=\"outer\")\n",
    "    print(result)\n",
    "    result = result.select(\n",
    "        [\n",
    "            \"block_id\",\n",
    "            (pl.col(\"bct\") / 1000 < pl.col(\"ddl\")).alias(\"intime\"),\n",
    "            # (\n",
    "            #     (pl.col(\"duration\") / 1000) < (pl.col(\"ddl\") + pl.col(\"start\") * 1000)\n",
    "            # ).alias(\"intime\"),\n",
    "            \"prio\",\n",
    "            \"ddl\",\n",
    "            (pl.col(\"ddl\") / 1e3 + pl.col(\"start\")).alias(\"timestamp\"),\n",
    "        ]\n",
    "    )\n",
    "    print(result)\n",
    "\n",
    "    x = result[\"timestamp\"].to_numpy()\n",
    "    y = np.zeros((8, len(x)))\n",
    "    y_count = np.zeros(8)\n",
    "    y_intime = np.zeros(8)\n",
    "    for i in range(len(x)):\n",
    "        y_count[result[\"prio\"][i]] += 1\n",
    "        if result[\"intime\"][i]:\n",
    "            y_intime[result[\"prio\"][i]] += 1\n",
    "        for j in range(8):\n",
    "            y[j][i] = y_intime[j] / y_count[j] if y_count[j] > 0 else 1\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    # ax.plot(x, y[0], label=\"prio 0\")\n",
    "    ax.plot(x, y[1], label=\"prio 1\")\n",
    "    ax.plot(x, y[2], label=\"prio 2\")\n",
    "    # ax.plot(x, y[3], label=\"prio 3\")\n",
    "    # ax.plot(x, y[4], label=\"prio 4\")\n",
    "    # ax.plot(x, y[5], label=\"prio 5\")\n",
    "    # ax.plot(x, y[6], label=\"prio 6\")\n",
    "    # ax.plot(x, y[7], label=\"prio 7\")\n",
    "\n",
    "    ax.set_ylim(0, 1.05)\n",
    "    ax.set_xlabel(\"time (s)\")\n",
    "    ax.set_ylabel(\"average intime ratio\")\n",
    "    ax.legend()\n",
    "\n",
    "    plt.savefig(f\"{os.path.basename(result_file_name).split('.')[0]}.png\")\n",
    "\n",
    "    result = result.groupby(\"prio\").agg([pl.count(), (pl.col(\"intime\") == True).sum()])\n",
    "    print(result)\n",
    "\n",
    "def hist(result_file_name, trace_file_name):\n",
    "    trace = parse_trace(trace_file_name)\n",
    "    # print(\n",
    "    #     trace.groupby(\"prio\").agg(\n",
    "    #         [\n",
    "    #             pl.count(),\n",
    "    #             pl.mean(\"size\").alias(\"size_mean\"),\n",
    "    #             pl.var(\"size\").alias(\"size_var\"),\n",
    "    #         ]\n",
    "    #     )\n",
    "    # )\n",
    "    # trace = trace.partition_by(\"prio\")\n",
    "    # prio_1 = trace[0][\"size\"].to_numpy()\n",
    "    # prio_2 = trace[1][\"size\"].to_numpy()\n",
    "\n",
    "    # fig, ax = plt.subplots()\n",
    "    # ax.hist(prio_1, 100, density=True, label=\"prio 1\")\n",
    "    # ax.hist(prio_2, 100, density=True, label=\"prio 2\")\n",
    "    # ax.set_xlabel(\"size (bytes)\")\n",
    "    # ax.set_ylabel(\"density\")\n",
    "    # trace = trace[\"size\"].to_numpy()\n",
    "    # fig, ax = plt.subplots()\n",
    "    # # ax.plot(trace)\n",
    "    # ax.scatter(np.arange(len(trace)), trace)\n",
    "    # plt.savefig(\"trace_size.png\")\n",
    "\n",
    "    # result = pl.read_csv(result_file_name)\n",
    "    # print(\n",
    "    #     result.groupby(\"priority\").agg(\n",
    "    #         [\n",
    "    #             pl.count(),\n",
    "    #             pl.mean(\"bct\").alias(\"bct_mean\"),\n",
    "    #             pl.var(\"bct\").alias(\"bct_var\"),\n",
    "    #         ]\n",
    "    #     )\n",
    "    # )\n",
    "    # result = result.partition_by(\"priority\")\n",
    "    # prio_1 = result[0][\"bct\"].to_numpy()\n",
    "    # prio_2 = result[1][\"bct\"].to_numpy()\n",
    "    # fig, ax = plt.subplots()\n",
    "    # ax.hist(prio_1, 100, density=True, label=\"prio 1\")\n",
    "    # ax.hist(prio_2, 100, density=True, label=\"prio 2\")\n",
    "    # ax.set_xlabel(\"bct (us)\")\n",
    "    # ax.set_ylabel(\"density\")\n",
    "    # plt.savefig(\"result_bct_hist.png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Style\n",
    "\n",
    "这里列出了所有可能的 style 选择，并绘制了样例图像。\n",
    "\n",
    "通过修改最后一个 plt.style.use() 函数中的数值来改变所有绘制图像的主题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solarize_Light2\n",
      "_classic_test_patch\n",
      "_mpl-gallery\n",
      "_mpl-gallery-nogrid\n",
      "bmh\n",
      "classic\n",
      "dark_background\n",
      "fast\n",
      "fivethirtyeight\n",
      "ggplot\n",
      "grayscale\n",
      "seaborn\n",
      "seaborn-bright\n",
      "seaborn-colorblind\n",
      "seaborn-dark\n",
      "seaborn-dark-palette\n",
      "seaborn-darkgrid\n",
      "seaborn-deep\n",
      "seaborn-muted\n",
      "seaborn-notebook\n",
      "seaborn-paper\n",
      "seaborn-pastel\n",
      "seaborn-poster\n",
      "seaborn-talk\n",
      "seaborn-ticks\n",
      "seaborn-white\n",
      "seaborn-whitegrid\n",
      "tableau-colorblind10\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for style in plt.style.available:\n",
    "    print(style)\n",
    "\n",
    "plt.style.use(\"seaborn-colorblind\")\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(5):\n",
    "    ax.plot(np.arange(10), np.random.randint(0, 10, 10), label=f\"L{i}\")\n",
    "fig.legend()\n",
    "\n",
    "plt.style.use(\"seaborn-colorblind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# trace hist\n",
    "\n",
    "用于绘制 trace file 当中数据的分布情况\n",
    "\n",
    "目前可以绘制数据的优先级统计图以及分布情况\n",
    "\n",
    "## 目前只能绘制只有两个优先级的 trace file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xml.dom.pulldom import PROCESSING_INSTRUCTION\n",
    "\n",
    "\n",
    "def trace_hist(trace_file_name, save=False, save_name=[\"distribute.svg\", \"data.svg\"]):\n",
    "    trace = parse_trace(trace_file_name)\n",
    "    # print the distribution of block_size, ddl and priority with block num\n",
    "    # print(\n",
    "    #     trace.groupby(\"prio\").agg(\n",
    "    #         [\n",
    "    #             pl.count(),\n",
    "    #             pl.mean(\"size\").alias(\"size_mean\"),\n",
    "    #             pl.var(\"size\").alias(\"size_var\"),\n",
    "    #         ]\n",
    "    #     )\n",
    "    # )\n",
    "    prio_trace = trace.partition_by(\"prio\")\n",
    "    prio_1 = prio_trace[0][\"size\"].to_numpy()\n",
    "    prio_2 = prio_trace[1][\"size\"].to_numpy()\n",
    "\n",
    "    # fig, ax = plt.subplots()\n",
    "    # ax.hist(prio_1, 100, label=\"prio 1\")\n",
    "    # ax.hist(prio_2, 100, density=True, label=\"prio 2\")\n",
    "    # ax.set_xlabel(\"size (bytes)\")\n",
    "    # ax.set_ylabel(\"density\")\n",
    "\n",
    "    # fig, ax = plt.subplots()\n",
    "    # ax.hist(prio_1, 100, density=True, label=\"prio 1\")\n",
    "    # ax.hist(prio_2, 100, density=True, label=\"prio 2\")\n",
    "    # ax.set_xlabel(\"size (bytes)\")\n",
    "    # ax.set_ylabel(\"density\")\n",
    "\n",
    "    # block size trend with block num\n",
    "    size_trace = trace[\"size\"].to_numpy()\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.bar([1], [len(prio_trace[0][\"prio\"])], color=[\"black\"], width=0.5, label=\"high priority\")\n",
    "    ax.bar([2], [len(prio_trace[1][\"prio\"])], color=[\"grey\"], width=0.5, label=\"low priority\")\n",
    "    ax.set_xlabel(\"priority\")\n",
    "    ax.set_ylabel(\"block number\")\n",
    "    ax.legend(ncol=2, bbox_to_anchor=(0.5, 1),\n",
    "              loc='lower center')\n",
    "    if save:\n",
    "        fig.savefig(save_name[0])\n",
    "\n",
    "    # distribution of trace\n",
    "    fig, ax = plt.subplots()\n",
    "    size = 28\n",
    "    # ax.scatter(np.arange(len(trace[:size])), size_trace[:size], c=[\"orange\" if trace[i][\"prio\"][0] == 1 else \"blue\" for i in range(len(size_trace[:size]))])\n",
    "    xarray = []\n",
    "    for i in range(size):\n",
    "        if trace[i][\"prio\"][0] == 1:\n",
    "            xarray.append(i)\n",
    "    ax.bar(xarray, size_trace[:len(xarray)], color=\"black\", label=\"high prioriy\") \n",
    "    xarray = []\n",
    "    for i in range(size):\n",
    "        if trace[i][\"prio\"][0] == 2:\n",
    "            xarray.append(i)\n",
    "    ax.bar(xarray, size_trace[:len(xarray)], color=\"grey\", label=\"low priority\")\n",
    "    ax.set_xlabel(\"block No.\")\n",
    "    ax.set_ylabel(\"size (bytes)\")\n",
    "    ax.set_xlim((0, 25))\n",
    "    ax.legend(ncol=2, bbox_to_anchor=(0.5, 1),\n",
    "              loc='lower center')\n",
    "    if save:\n",
    "        fig.savefig(save_name[1])\n",
    "    # ax.plot(size_trace)\n",
    "    # ax.scatter(prio_trace[0][\"id\"].to_numpy(), prio_trace[0][\"size\"].to_numpy(), c=[\"red\"])\n",
    "    # ax.scatter(prio_trace[1][\"id\"].to_numpy(), prio_trace[1][\"size\"].to_numpy(), c=[\"yellow\"])\n",
    "    # plt.savefig(\"trace_size.png\")\n",
    "\n",
    "    # result = pl.read_csv(result_file_name)\n",
    "    # print(\n",
    "    #     result.groupby(\"priority\").agg(\n",
    "    #         [\n",
    "    #             pl.count(),\n",
    "    #             pl.mean(\"bct\").alias(\"bct_mean\"),\n",
    "    #             pl.var(\"bct\").alias(\"bct_var\"),\n",
    "    #         ]\n",
    "    #     )\n",
    "    # )\n",
    "    # result = result.partition_by(\"priority\")\n",
    "    # prio_1 = result[0][\"bct\"].to_numpy()\n",
    "    # prio_2 = result[1][\"bct\"].to_numpy()\n",
    "    # fig, ax = plt.subplots()\n",
    "    # ax.hist(prio_1, 100, density=True, label=\"prio 1\")\n",
    "    # ax.hist(prio_2, 100, density=True, label=\"prio 2\")\n",
    "    # ax.set_xlabel(\"bct (us)\")\n",
    "    # ax.set_ylabel(\"density\")\n",
    "    # plt.savefig(\"result_bct_hist.png\")\n",
    "trace_hist(\"data/trace_1300_1ms_1000_seq012.txt\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get stats\n",
    "\n",
    "利用 trace file 和一个客户端的输出来生成一些统计数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "stats of data/client_n_n_fifo.csv\n",
      "------------\n",
      "prio 1 avg_bct 6272.96\n",
      "prio 1 avg_bcr 0.322\n",
      "------------\n",
      "prio 2 avg_bct 6253.49\n",
      "prio 2 avg_bcr 0.288\n",
      "-------------------------\n",
      "ideal throughput:  16666666.666666664 Mbps\n",
      "throughput: 79.15136283311222 Mbps\n",
      "goodput:  24.141165664099226 Mbps\n",
      "avg_bcr(block completion rate): 0.305\n",
      "avg_bct(block completion time): 6263.225\n"
     ]
    }
   ],
   "source": [
    "def get_prio_groups(df, prio_col_name):\n",
    "    '''\n",
    "    Group trace rows by their priorities.\n",
    "\n",
    "    Results are like: \n",
    "\n",
    "    [DataFrame(priority 1), Dataframe(priority2), ...]\n",
    "    '''\n",
    "    prio_list = df.partition_by(prio_col_name)\n",
    "    # sort\n",
    "    prio_list.sort(key=lambda x: x[prio_col_name][0])\n",
    "    return prio_list\n",
    "# good put and avg bct\n",
    "def get_stats(result_file_path, trace_file_path):\n",
    "    '''\n",
    "    Print some basic stats like Throughput, block completion time (BCT) and block completion rate (BCR)\n",
    "    '''\n",
    "    print(\"===============================\")\n",
    "    print(\"stats of %s\" % result_file_path)\n",
    "    trace = parse_trace(trace_file_path)\n",
    "    trace_prios = get_prio_groups(trace, \"prio\")\n",
    "    trace_size = len(trace)\n",
    "\n",
    "    all_bytes = np.sum(trace[\"size\"].to_numpy())\n",
    "    all_time = trace[\"start\"][-1] + trace[\"ddl\"][-1] * 1e-3\n",
    "    ideal_throughput = all_bytes / all_time\n",
    "    result = parse_result(result_file_path)\n",
    "    in_time = result.filter(pl.col(\"bct\") <= pl.col(\"deadline\"))\n",
    "    good_bytes = np.sum(in_time[\"size\"].to_numpy())\n",
    "    total_bytes = np.sum(result[\"size\"].to_numpy())\n",
    "    finish_time = result[\"duration\"][-1] # micro\n",
    "    throughput = total_bytes * 8 / finish_time # Mbps\n",
    "    goodput = good_bytes * 8 / finish_time # Mbps\n",
    "    avg_bct = np.average(result[\"bct\"].to_numpy()) #ms\n",
    "    avg_bcr = 0\n",
    "    \n",
    "    prio_result = get_prio_groups(result, \"priority\")\n",
    "\n",
    "    for idx, prio_frame in enumerate(prio_result):\n",
    "        print(\"------------\")\n",
    "        avg_prio_bct = np.average(prio_frame[\"bct\"].to_numpy()) #ms\n",
    "        print(\"prio\", prio_frame[\"priority\"][0], \"avg_bct\", avg_prio_bct)\n",
    "        print(\"prio\", prio_frame[\"priority\"][0], \"avg_bcr\", len(prio_frame.filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / len(trace_prios[idx]))\n",
    "        avg_bcr += len(prio_frame.filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / len(trace_prios[idx])\n",
    "    avg_bcr /= len(trace_prios)\n",
    "    print(\"-------------------------\")\n",
    "    print(\"ideal throughput: \", ideal_throughput, \"Mbps\")\n",
    "    print(\"throughput:\", throughput, \"Mbps\")\n",
    "    print(\"goodput: \", goodput, \"Mbps\")\n",
    "    print(\"avg_bcr(block completion rate):\", avg_bcr)\n",
    "    print(\"avg_bct(block completion time):\", avg_bct)\n",
    "# test\n",
    "get_stats(\"data/client_n_n_fifo.csv\", \"data/trace_1300_1ms_1000_seq012.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get table stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats of ['data/client_n_n_fifo.csv', 'data/client_n_n.csv']\n",
      "|\t| QUIC | DTP |\n",
      "| Throughput (Mbps) | 79.15 | 37.23 |\n",
      "| Goodput (Mbps) | 24.14 | 37.08 |\n",
      "| 平均块完成时间 (ms) | 6263 | 3040 |\n",
      "| 高优先级块平均完成时间 (ms) | 6272 | 2495 |\n",
      "| 低优先级块平均完成时间 (ms) | 6253 | 4487 |\n"
     ]
    }
   ],
   "source": [
    "# good put and avg bct\n",
    "def get_table_stats(result_file_paths, labels=[]):\n",
    "    '''\n",
    "    Print some basic stats like Throughput, block completion time (BCT) and block completion rate (BCR)\n",
    "    in a table-like format\n",
    "\n",
    "    Parameters:\n",
    "    - result_file_paths: a list of result .csv file paths\n",
    "    - labels: the label name of each result file, using result_file_path at default\n",
    "    '''\n",
    "    print(\"stats of %s\" % str(result_file_paths))\n",
    "    while len(labels) < len(result_file_paths):\n",
    "        labels.append(result_file_paths[len(labels)])\n",
    "    result_throughput = []\n",
    "    result_goodput = []\n",
    "    result_avg_bct = []\n",
    "    result_prio1_bct = []\n",
    "    result_prio2_bct = []\n",
    "    for result_file_path in result_file_paths:\n",
    "        result = parse_result(result_file_path)\n",
    "        in_time = result.filter(pl.col(\"bct\") <= pl.col(\"deadline\"))\n",
    "        good_bytes = np.sum(in_time[\"size\"].to_numpy())\n",
    "        total_bytes = np.sum(result[\"size\"].to_numpy())\n",
    "        finish_time = result[\"duration\"][-1] # micro\n",
    "        throughput = total_bytes * 8 / finish_time # Mbps\n",
    "        goodput = good_bytes * 8 / finish_time # Mbps\n",
    "        avg_bct = np.average(result[\"bct\"].to_numpy()) #ms\n",
    "\n",
    "        result_throughput.append(throughput)\n",
    "        result_goodput.append(goodput)\n",
    "        result_avg_bct.append(avg_bct)\n",
    "\n",
    "        prio_result = result.partition_by(\"priority\")\n",
    "        for idx, prio_frame in enumerate(prio_result):\n",
    "            avg_prio_bct = np.average(prio_frame[\"bct\"].to_numpy()) #ms\n",
    "            if prio_frame[\"priority\"][0] == 1:\n",
    "                result_prio1_bct.append(avg_prio_bct)\n",
    "            elif prio_frame[\"priority\"][0] == 2:\n",
    "                result_prio2_bct.append(avg_prio_bct)\n",
    "            else:\n",
    "                print(\"priority %d is not expected\" % (prio_frame[\"priority\"][0]))\n",
    "    print(\"|\\t| %s |\" % (\" | \".join(labels)))\n",
    "    print(\"| Throughput (Mbps) | %s |\" % (\" | \".join([x for x in map(lambda x: \"%0.2f\" % (x), result_throughput)])))\n",
    "    print(\"| Goodput (Mbps) | %s |\" % (\" | \".join([x for x in map(lambda x: \"%0.2f\" % (x), result_goodput)])))\n",
    "    print(\"| 平均块完成时间 (ms) | %s |\" % (\" | \".join([x for x in map(lambda x: \"%d\" % (int(x)), result_avg_bct)])))\n",
    "    print(\"| 高优先级块平均完成时间 (ms) | %s |\" % (\" | \".join([x for x in map(lambda x: \"%d\" % (int(x)), result_prio1_bct)])))\n",
    "    print(\"| 低优先级块平均完成时间 (ms) | %s |\" % (\" | \".join([x for x in map(lambda x: \"%d\" % (int(x)), result_prio2_bct)])))\n",
    "# test\n",
    "get_table_stats([\"data/client_n_n_fifo.csv\", \"data/client_n_n.csv\"], labels=[\"QUIC\", \"DTP\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# draw figs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_in_time_rate(result_file_path1, result_file_path2, trace_file_path, label1=\"QUIC\", label2=\"DTP\"):\n",
    "    trace = parse_trace(trace_file_path)\n",
    "    trace_prio1, trace_prio2 = trace.partition_by(\"prio\")\n",
    "    trace_size = len(trace)\n",
    "    result1_bcr = []\n",
    "    result2_bcr = []\n",
    "    for result_file_path in [result_file_path1, result_file_path2]:\n",
    "        result = parse_result(result_file_path)\n",
    "        result_prio2, result_prio1 = result.partition_by(\"priority\")\n",
    "        if result_file_path1 == result_file_path:\n",
    "            result_bcr = result1_bcr\n",
    "        else:\n",
    "            result_bcr = result2_bcr\n",
    "        # total_bcr = len(result.filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / trace_size\n",
    "        prio1_bcr = len(result_prio1.filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / len(trace_prio1)\n",
    "        prio2_bcr = len(result_prio2.filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / len(trace_prio2)\n",
    "        # result_bcr.append(total_bcr)\n",
    "        result_bcr.append(prio1_bcr)\n",
    "        result_bcr.append(prio2_bcr)\n",
    "\n",
    "    labels = [\"High Priority\", \"Low Priority\"]\n",
    "    # quic_bcr = [len(results[0].filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / trace_size, 0, 0]\n",
    "    # dtp_bcr = [len(results[1].filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / trace_size, 0, 0]\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.30  # the width of the bars\n",
    "    fig, ax = plt.subplots()\n",
    "    rects1 = ax.bar(x - width/2, result1_bcr, width, label=label1)\n",
    "    rects2 = ax.bar(x + width/2, result2_bcr, width, label=label2)\n",
    "    ax.set_ylabel('Block Completion Rate')\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(labels)\n",
    "    ax.legend()\n",
    "\n",
    "def draw_avg_bct(result_file_path1, result_file_path2, trace_file_path, label1=\"QUIC\", label2=\"DTP\"):\n",
    "    trace = parse_trace(trace_file_path)\n",
    "    trace_prio1, trace_prio2 = trace.partition_by(\"prio\")\n",
    "    trace_size = len(trace)\n",
    "    result1_bct = []\n",
    "    result2_bct = []\n",
    "    for result_file_path in [result_file_path1, result_file_path2]:\n",
    "        result = parse_result(result_file_path)\n",
    "        result_prio2, result_prio1 = result.partition_by(\"priority\")\n",
    "        if result_file_path1 == result_file_path:\n",
    "            result_bcr = result1_bct\n",
    "        else:\n",
    "            result_bcr = result2_bct\n",
    "        # total_bct = np.average(result[\"bct\"].to_numpy())\n",
    "        prio1_bct = np.average(result_prio1[\"bct\"].to_numpy())\n",
    "        prio2_bct = np.average(result_prio2[\"bct\"].to_numpy())\n",
    "        # result_bcr.append(total_bct)\n",
    "        result_bcr.append(prio1_bct)\n",
    "        result_bcr.append(prio2_bct)\n",
    "\n",
    "    labels = [\"High Priority\", \"Low Priority\"]\n",
    "    # quic_bcr = [len(results[0].filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / trace_size, 0, 0]\n",
    "    # dtp_bcr = [len(results[1].filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / trace_size, 0, 0]\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.30  # the width of the bars\n",
    "    fig, ax = plt.subplots()\n",
    "    rects1 = ax.bar(x - width/2, result1_bct, width, label=label1)\n",
    "    rects2 = ax.bar(x + width/2, result2_bct, width, label=label2)\n",
    "    ax.set_ylabel('Average BCT')\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(labels)\n",
    "    ax.legend()\n",
    "\n",
    "def draw_cmp_fig(result_file_paths, trace_file_path, labels=[], width=0.2, with_total=True, save=False, save_name=[\"bcr.svg\", \"bct.svg\"], ylabel=[\"Block Completion Rate\", \"Average BCT\"]):\n",
    "    '''\n",
    "    Draw comparision figures of given result files of BCR and average BCT\n",
    "\n",
    "    Parameters:\n",
    "    - result_file_paths(list of str): a list of result .csv file paths\n",
    "    - trace_file_path(str): the path of trace file\n",
    "    - labels(list): the xlabel name of each result file, using result_file_path at default\n",
    "    - width(float): the width of hist\n",
    "    - with_total(bool): traw the figure with the Total statistic hist\n",
    "    - save(bool): save the figure with the given path of save_name\n",
    "    - save_name(list of str): two paths of to save the figures\n",
    "    - ylabel: the ylabel of each figure\n",
    "    '''\n",
    "    if not trace_file_path:\n",
    "        print(\"no trace file path\")\n",
    "        return\n",
    "    while len(labels) < len(result_file_paths):\n",
    "        labels.append(result_file_paths[len(labels)])\n",
    "    trace = parse_trace(trace_file_path)\n",
    "    trace_prios = get_prio_groups(trace, \"prio\")\n",
    "    trace_size = len(trace)\n",
    "    bcrs = [[] for r in result_file_paths]\n",
    "    bcts = [[] for r in result_file_paths]\n",
    "\n",
    "    for idx, result_file_path in enumerate(result_file_paths):\n",
    "        result = parse_result(result_file_path)\n",
    "        result_prios = get_prio_groups(result, \"priority\")\n",
    "        \n",
    "        result_bcr = bcrs[idx]\n",
    "        total_bcr = len(result.filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / trace_size\n",
    "        if with_total:\n",
    "            result_bcr.append(total_bcr)\n",
    "\n",
    "        for i, result_prio in enumerate(result_prios):\n",
    "            prio_bcr = len(result_prio.filter(pl.col(\"bct\") <= pl.col(\"deadline\"))) / len(trace_prios[i])\n",
    "            result_bcr.append(prio_bcr)\n",
    "\n",
    "        result_bct = bcts[idx]\n",
    "        total_bct = np.average(result[\"bct\"].to_numpy())\n",
    "        if with_total:\n",
    "            result_bct.append(total_bct)\n",
    "\n",
    "        for i, result_prio in enumerate(result_prios):\n",
    "            prio_bct = np.average(result_prio[\"bct\"].to_numpy())\n",
    "            result_bct.append(prio_bct)\n",
    "    \n",
    "    # if with_total:\n",
    "    #     x_labels = [\"Total\", \"High Priority\", \"Low Priority\"]\n",
    "    # else:\n",
    "    #     x_labels = [\"High Priority\", \"Low Priority\"]\n",
    "    if with_total:\n",
    "        x_labels = [\"Total\"]\n",
    "    else:\n",
    "        x_labels = []\n",
    "\n",
    "    for i in range(len(trace_prios)):\n",
    "        x_labels.append(\"prio %d\" % i)\n",
    "\n",
    "    x = np.arange(len(x_labels))  # the label locations\n",
    "    fig, ax = plt.subplots()\n",
    "    for idx, bcr in enumerate(bcrs):\n",
    "        ax.bar(x + width * (idx - len(bcrs) // 2), bcr, width, label=labels[idx])\n",
    "    ax.set_ylabel(ylabel[0])\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(x_labels)\n",
    "    ax.legend(ncol=len(x_labels) + 1, bbox_to_anchor=(0.5, 1),\n",
    "              loc='lower center')\n",
    "    if save:\n",
    "        fig.savefig(save_name[0])\n",
    "\n",
    "    x = np.arange(len(x_labels))  # the label locations\n",
    "    fig, ax = plt.subplots()\n",
    "    for idx, bct in enumerate(bcts):\n",
    "        ax.bar(x + width * (idx - len(bcts) // 2), bct, width, label=labels[idx])\n",
    "    ax.set_ylabel(ylabel[1])\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(x_labels)\n",
    "    # ax.legend()\n",
    "    ax.legend(ncol=len(x_labels) + 1, bbox_to_anchor=(0.5, 1),\n",
    "              loc='lower center')\n",
    "    if save:\n",
    "        fig.savefig(save_name[1])\n",
    "# test        \n",
    "draw_cmp_fig([\"data/client_n_n_fifo.csv\", \"data/client_n_n.csv\"], \"data/trace_1300_1ms_1000_seq012.txt\", [\"QUIC\", \"DTP\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# original data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## trace hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace_hist(\"data/trace_1300_1ms_1000_seq012.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fifo on/off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "stats of data/client_n_n_fifo.csv\n",
      "------------\n",
      "prio 1 avg_bct 6272.96\n",
      "prio 1 avg_bcr 0.322\n",
      "------------\n",
      "prio 2 avg_bct 6253.49\n",
      "prio 2 avg_bcr 0.288\n",
      "-------------------------\n",
      "ideal throughput:  16666666.666666664 Mbps\n",
      "throughput: 79.15136283311222 Mbps\n",
      "goodput:  24.141165664099226 Mbps\n",
      "avg_bcr(block completion rate): 0.305\n",
      "avg_bct(block completion time): 6263.225\n",
      "===============================\n",
      "stats of data/client_n_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 2495.497142857143\n",
      "prio 1 avg_bcr 0.35\n",
      "------------\n",
      "prio 2 avg_bct 4487.272727272727\n",
      "prio 2 avg_bcr 0.13\n",
      "-------------------------\n",
      "ideal throughput:  16666666.666666664 Mbps\n",
      "throughput: 37.23159170075378 Mbps\n",
      "goodput:  37.07710376838551 Mbps\n",
      "avg_bcr(block completion rate): 0.24\n",
      "avg_bct(block completion time): 3040.9626556016597\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_stats(\"data/client_n_n_fifo.csv\", \"data/trace_1300_1ms_1000_seq012.txt\")\n",
    "get_stats(\"data/client_n_n.csv\",  \"data/trace_1300_1ms_1000_seq012.txt\")\n",
    "draw_cmp_fig([\"data/client_n_n_fifo.csv\", \"data/client_n_n.csv\"], \"data/trace_1300_1ms_1000_seq012.txt\", [\"QUIC\", \"DTP\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tos on/off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "stats of data/client_t_n_fifo.csv\n",
      "------------\n",
      "prio 1 avg_bct 6654.048\n",
      "prio 1 avg_bcr 0.312\n",
      "------------\n",
      "prio 2 avg_bct 6785.242\n",
      "prio 2 avg_bcr 0.272\n",
      "-------------------------\n",
      "ideal throughput:  16666666.666666664 Mbps\n",
      "throughput: 70.32124414454778 Mbps\n",
      "goodput:  20.533803290207953 Mbps\n",
      "avg_bcr(block completion rate): 0.29200000000000004\n",
      "avg_bct(block completion time): 6719.645\n",
      "===============================\n",
      "stats of data/client_t_t_fifo.csv\n",
      "------------\n",
      "prio 1 avg_bct 3468.5\n",
      "prio 1 avg_bcr 0.818\n",
      "------------\n",
      "prio 2 avg_bct 3425.816\n",
      "prio 2 avg_bcr 0.818\n",
      "-------------------------\n",
      "ideal throughput:  16666666.666666664 Mbps\n",
      "throughput: 125.96663647666279 Mbps\n",
      "goodput:  103.04070863791016 Mbps\n",
      "avg_bcr(block completion rate): 0.818\n",
      "avg_bct(block completion time): 3447.158\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_stats(\"data/client_t_n_fifo.csv\", \"data/trace_1300_1ms_1000_seq012.txt\")\n",
    "get_stats(\"data/client_t_t_fifo.csv\", \"data/trace_1300_1ms_1000_seq012.txt\")\n",
    "draw_cmp_fig([\"data/client_t_n_fifo.csv\", \"data/client_t_t_fifo.csv\"], \"data/trace_1300_1ms_1000_seq012.txt\", [\"QUIC\", \"DTP\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## original data all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "stats of data/client_n_n_fifo.csv\n",
      "------------\n",
      "prio 1 avg_bct 6272.96\n",
      "prio 1 avg_bcr 0.322\n",
      "------------\n",
      "prio 2 avg_bct 6253.49\n",
      "prio 2 avg_bcr 0.288\n",
      "-------------------------\n",
      "ideal throughput:  16666666.666666664 Mbps\n",
      "throughput: 79.15136283311222 Mbps\n",
      "goodput:  24.141165664099226 Mbps\n",
      "avg_bcr(block completion rate): 0.305\n",
      "avg_bct(block completion time): 6263.225\n",
      "===============================\n",
      "stats of data/client_n_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 2495.497142857143\n",
      "prio 1 avg_bcr 0.35\n",
      "------------\n",
      "prio 2 avg_bct 4487.272727272727\n",
      "prio 2 avg_bcr 0.13\n",
      "-------------------------\n",
      "ideal throughput:  16666666.666666664 Mbps\n",
      "throughput: 37.23159170075378 Mbps\n",
      "goodput:  37.07710376838551 Mbps\n",
      "avg_bcr(block completion rate): 0.24\n",
      "avg_bct(block completion time): 3040.9626556016597\n",
      "===============================\n",
      "stats of data/client_t_t_fifo.csv\n",
      "------------\n",
      "prio 1 avg_bct 3468.5\n",
      "prio 1 avg_bcr 0.818\n",
      "------------\n",
      "prio 2 avg_bct 3425.816\n",
      "prio 2 avg_bcr 0.818\n",
      "-------------------------\n",
      "ideal throughput:  16666666.666666664 Mbps\n",
      "throughput: 125.96663647666279 Mbps\n",
      "goodput:  103.04070863791016 Mbps\n",
      "avg_bcr(block completion rate): 0.818\n",
      "avg_bct(block completion time): 3447.158\n",
      "===============================\n",
      "stats of data/client_t_t.csv\n",
      "------------\n",
      "prio 1 avg_bct 2349.066889632107\n",
      "prio 1 avg_bcr 0.594\n",
      "------------\n",
      "prio 2 avg_bct 4305.151515151515\n",
      "prio 2 avg_bcr 0.586\n",
      "-------------------------\n",
      "ideal throughput:  16666666.666666664 Mbps\n",
      "throughput: 80.46346143753172 Mbps\n",
      "goodput:  79.65342659084516 Mbps\n",
      "avg_bcr(block completion rate): 0.59\n",
      "avg_bct(block completion time): 3323.827181208054\n"
     ]
    }
   ],
   "source": [
    "get_stats(\"data/client_n_n_fifo.csv\", \"data/trace_1300_1ms_1000_seq012.txt\")\n",
    "get_stats(\"data/client_n_n.csv\", \"data/trace_1300_1ms_1000_seq012.txt\")\n",
    "get_stats(\"data/client_t_t_fifo.csv\", \"data/trace_1300_1ms_1000_seq012.txt\")\n",
    "get_stats(\"data/client_t_t.csv\", \"data/trace_1300_1ms_1000_seq012.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_cmp_fig([\"data/client_n_n_fifo.csv\", \"data/client_n_n.csv\", \"data/client_t_t_fifo.csv\", \"data/client_t_t.csv\"], \"data/trace_1300_1ms_1000_seq012.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 25 data 100000 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "stats of 25data/client_25_100000_1_n_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 38951.829\n",
      "prio 1 avg_bcr 0.029\n",
      "------------\n",
      "prio 2 avg_bct 38162.348\n",
      "prio 2 avg_bcr 0.039\n",
      "-------------------------\n",
      "ideal throughput:  37500000.00000103 Mbps\n",
      "throughput: 30.29509046473767 Mbps\n",
      "goodput:  1.0805248932423102 Mbps\n",
      "avg_bcr(block completion rate): 0.034\n",
      "avg_bct(block completion time): 38425.50833333333\n",
      "===============================\n",
      "stats of 25data/client_25_100000_1_n_t.csv\n",
      "------------\n",
      "prio 1 avg_bct 6436.2402234636875\n",
      "prio 1 avg_bcr 0.133\n",
      "------------\n",
      "prio 2 avg_bct 6711.848441926346\n",
      "prio 2 avg_bcr 0.12\n",
      "-------------------------\n",
      "ideal throughput:  37500000.00000103 Mbps\n",
      "throughput: 51.024127122968146 Mbps\n",
      "goodput:  17.887217497055563 Mbps\n",
      "avg_bcr(block completion rate): 0.1265\n",
      "avg_bct(block completion time): 6619.1156015037595\n",
      "===============================\n",
      "stats of 25data/client_25_100000_1_t_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 4422.523809523809\n",
      "prio 1 avg_bcr 0.088\n",
      "------------\n",
      "prio 2 avg_bct 4502.261261261261\n",
      "prio 2 avg_bcr 0.029\n",
      "-------------------------\n",
      "ideal throughput:  37500000.00000103 Mbps\n",
      "throughput: 15.82766544021738 Mbps\n",
      "goodput:  9.750376178361762 Mbps\n",
      "avg_bcr(block completion rate): 0.058499999999999996\n",
      "avg_bct(block completion time): 4459.869198312236\n",
      "===============================\n",
      "stats of 25data/client_25_100000_1_t_t.csv\n",
      "------------\n",
      "prio 1 avg_bct 3296.887755102041\n",
      "prio 1 avg_bcr 0.193\n",
      "------------\n",
      "prio 2 avg_bct 4062.5204081632655\n",
      "prio 2 avg_bcr 0.0965\n",
      "-------------------------\n",
      "ideal throughput:  37500000.00000103 Mbps\n",
      "throughput: 37.89352595875696 Mbps\n",
      "goodput:  37.31352301040864 Mbps\n",
      "avg_bcr(block completion rate): 0.14475\n",
      "avg_bct(block completion time): 3679.704081632653\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_n_file = \"25data/client_25_100000_1_n_n.csv\"\n",
    "n_t_file = \"25data/client_25_100000_1_n_t.csv\" \n",
    "t_n_file = \"25data/client_25_100000_1_t_n.csv\" \n",
    "t_t_file = \"25data/client_25_100000_1_t_t.csv\" \n",
    "trace_file = \"trace_tos_2.txt\" \n",
    "get_stats(n_n_file, trace_file)\n",
    "get_stats(n_t_file, trace_file)\n",
    "get_stats(t_n_file, trace_file)\n",
    "get_stats(t_t_file, trace_file)\n",
    "draw_cmp_fig([n_n_file, n_t_file, t_n_file, t_t_file], trace_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 25 data 10000 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "stats of 25data/client_25_10000_1_n_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 1891.851\n",
      "prio 1 avg_bcr 0.923\n",
      "------------\n",
      "prio 2 avg_bct 1826.19\n",
      "prio 2 avg_bcr 0.927\n",
      "-------------------------\n",
      "ideal throughput:  3750000.000000103 Mbps\n",
      "throughput: 32.137045215617476 Mbps\n",
      "goodput:  29.74819152125658 Mbps\n",
      "avg_bcr(block completion rate): 0.925\n",
      "avg_bct(block completion time): 1848.077\n",
      "===============================\n",
      "stats of 25data/client_25_10000_1_n_t.csv\n",
      "------------\n",
      "prio 1 avg_bct 1936.48\n",
      "prio 1 avg_bcr 0.925\n",
      "------------\n",
      "prio 2 avg_bct 1963.763\n",
      "prio 2 avg_bcr 0.9195\n",
      "-------------------------\n",
      "ideal throughput:  3750000.000000103 Mbps\n",
      "throughput: 32.99986057558907 Mbps\n",
      "goodput:  30.40387154364273 Mbps\n",
      "avg_bcr(block completion rate): 0.92225\n",
      "avg_bct(block completion time): 1954.6686666666667\n",
      "===============================\n",
      "stats of 25data/client_25_10000_1_t_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 1045.301\n",
      "prio 1 avg_bcr 1.0\n",
      "------------\n",
      "prio 2 avg_bct 2192.016\n",
      "prio 2 avg_bcr 1.0\n",
      "-------------------------\n",
      "ideal throughput:  3750000.000000103 Mbps\n",
      "throughput: 32.4292508599832 Mbps\n",
      "goodput:  32.4292508599832 Mbps\n",
      "avg_bcr(block completion rate): 1.0\n",
      "avg_bct(block completion time): 1809.7776666666666\n",
      "===============================\n",
      "stats of 25data/client_25_10000_1_t_t.csv\n",
      "------------\n",
      "prio 1 avg_bct 812.935\n",
      "prio 1 avg_bcr 1.0\n",
      "------------\n",
      "prio 2 avg_bct 1793.3705\n",
      "prio 2 avg_bcr 1.0\n",
      "-------------------------\n",
      "ideal throughput:  3750000.000000103 Mbps\n",
      "throughput: 35.10496384188724 Mbps\n",
      "goodput:  35.10496384188724 Mbps\n",
      "avg_bcr(block completion rate): 1.0\n",
      "avg_bct(block completion time): 1466.5586666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_n_file = \"25data/client_25_10000_1_n_n.csv\"\n",
    "n_t_file = \"25data/client_25_10000_1_n_t.csv\" \n",
    "t_n_file = \"25data/client_25_10000_1_t_n.csv\" \n",
    "t_t_file = \"25data/client_25_10000_1_t_t.csv\" \n",
    "trace_file = \"trace_tos_1.txt\"\n",
    "get_stats(n_n_file, trace_file)\n",
    "get_stats(n_t_file, trace_file)\n",
    "get_stats(t_n_file, trace_file)\n",
    "get_stats(t_t_file, trace_file)\n",
    "draw_cmp_fig([n_n_file, n_t_file, t_n_file, t_t_file], trace_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 25 data 30000 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "stats of 25data/client_25_30000_1_n_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 10333.634\n",
      "prio 1 avg_bcr 0.2\n",
      "------------\n",
      "prio 2 avg_bct 10676.9095\n",
      "prio 2 avg_bcr 0.1765\n",
      "-------------------------\n",
      "ideal throughput:  11250000.00000031 Mbps\n",
      "throughput: 28.793262376603874 Mbps\n",
      "goodput:  5.307558031420648 Mbps\n",
      "avg_bcr(block completion rate): 0.18825\n",
      "avg_bct(block completion time): 10562.484333333334\n",
      "===============================\n",
      "stats of 25data/client_25_30000_1_n_t.csv\n",
      "------------\n",
      "prio 1 avg_bct 2620.65\n",
      "prio 1 avg_bcr 0.355\n",
      "------------\n",
      "prio 2 avg_bct 2685.5837912087914\n",
      "prio 2 avg_bcr 0.359\n",
      "-------------------------\n",
      "ideal throughput:  11250000.00000031 Mbps\n",
      "throughput: 41.162506618837114 Mbps\n",
      "goodput:  40.595008825378876 Mbps\n",
      "avg_bcr(block completion rate): 0.357\n",
      "avg_bct(block completion time): 2664.098345588235\n",
      "===============================\n",
      "stats of 25data/client_25_30000_1_t_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 4163.215568862275\n",
      "prio 1 avg_bcr 0.379\n",
      "------------\n",
      "prio 2 avg_bct 4857.480456026059\n",
      "prio 2 avg_bcr 0.1975\n",
      "-------------------------\n",
      "ideal throughput:  11250000.00000031 Mbps\n",
      "throughput: 24.064631340819027 Mbps\n",
      "goodput:  16.704954850039396 Mbps\n",
      "avg_bcr(block completion rate): 0.28825\n",
      "avg_bct(block completion time): 4545.528251121076\n",
      "===============================\n",
      "stats of 25data/client_25_30000_1_t_t.csv\n",
      "------------\n",
      "prio 1 avg_bct 2338.5407407407406\n",
      "prio 1 avg_bcr 0.54\n",
      "------------\n",
      "prio 2 avg_bct 3538.98202247191\n",
      "prio 2 avg_bcr 0.439\n",
      "-------------------------\n",
      "ideal throughput:  11250000.00000031 Mbps\n",
      "throughput: 45.273753847180764 Mbps\n",
      "goodput:  44.893834234477154 Mbps\n",
      "avg_bcr(block completion rate): 0.48950000000000005\n",
      "avg_bct(block completion time): 3085.6685314685315\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_n_file = \"25data/client_25_30000_1_n_n.csv\"\n",
    "n_t_file = \"25data/client_25_30000_1_n_t.csv\" \n",
    "t_n_file = \"25data/client_25_30000_1_t_n.csv\" \n",
    "t_t_file = \"25data/client_25_30000_1_t_t.csv\" \n",
    "trace_file = \"trace_tos_3.txt\"\n",
    "get_stats(n_n_file, trace_file)\n",
    "get_stats(n_t_file, trace_file)\n",
    "get_stats(t_n_file, trace_file)\n",
    "get_stats(t_t_file, trace_file)\n",
    "draw_cmp_fig([n_n_file, n_t_file, t_n_file, t_t_file], trace_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Paper result 30000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_n_file = \"25data/client_25_30000_1_n_n.csv\"\n",
    "n_t_file = \"25data/client_25_30000_1_n_t.csv\" \n",
    "t_n_file = \"25data/client_25_30000_1_t_n.csv\" \n",
    "t_t_file = \"25data/client_25_30000_1_t_t.csv\" \n",
    "trace_file = \"trace_tos_3.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## trace hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace_hist(trace_file, save=True, save_name=[\"new_imgs/trace.svg\", \"new_imgs/trace_distribute.svg\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fifo on/off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats of ['25data/client_25_30000_1_n_n.csv', '25data/client_25_30000_1_t_n.csv']\n",
      "|\t| QUIC | DTP |\n",
      "| Throughput (Mbps) | 28.79 | 24.06 |\n",
      "| Goodput (Mbps) | 5.31 | 16.70 |\n",
      "| 平均块完成时间 (ms) | 10562 | 4545 |\n",
      "| 高优先级块平均完成时间 (ms) | 10333 | 4163 |\n",
      "| 低优先级块平均完成时间 (ms) | 10676 | 4857 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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PKikpSbfccss5z5eYmKi2bdu6WmRkpBerBQAATU2gPz/8yJEjOnnypGw2m1u/zWZTUVHRWd9nGIby8vIkSdnZ2erVq5emT5+uL774wvW+X85hs9mUlZVV73xOp1NOp/M89wYAADQXfj0DVFNTo927dysmJsbVZ7FYFBMTo4yMjAbPExAQ4LrCy263q7Cw0G1Oq9WqQYMGeTQnAABoufx6BkiSkpKSlJKSol27dmnHjh2aMGGCQkNDlZycLElKSUlRfn6+ZsyYIUmaNm2adu3apby8PIWEhOj222/XI488otGjR7vmfP311zVz5kz9+OOPstvtev7551VQUKC1a9f6YxcBAEAT4/cAtGrVKoWHh2vu3LmKiIhQVlaWYmNjdfjwYUlSVFSUamtrXeNDQ0O1dOlSdenSRZWVlcrNzdXDDz+sVatWucbMnz9foaGhevvttxUWFqYvv/xSsbGxqq6ubvT9AwAATY/f7wPUFHEfIKCF4z5A/q4A8Ilmcx8gAAAAfyAAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0zmnANSqVSvFxMRo1KhRatOmjSTp4osvVmhoqFeLAwAA8AWPnwUWFRWljRs3KioqSiEhIdq0aZOOHz+uqVOnKiQkxO2hpAAAAE2Rx2eAFi5cqF27dqldu3aqrKx09a9Zs0YxMTFeLQ4AAMAXPD4DdPPNN+uGG25QTU2NW/+BAwcUGRnptcIAAAB8xeMzQAEBAWrVqlWd/i5duvDkdAAA0Cx4HIDS0tI0YcIE12vDMBQaGqqEhAR99tln3qwNAADAJzz+CmzSpElKTU3Vt99+q9atW+uDDz7Q5ZdfriNHjuiBBx7wRY0AAABe5XEAys/PV79+/XTfffepX79+atOmjd555x29//77qqqq8kWNAAAAXnVOi6C/+uorffDBB/rggw9c/a1atdLNN9+sf/3rX14tEAAAwNs8XgO0ZcsWtW/fvk7/RRddpC1btnilKAAAAF/yOABZLBYZhlGnv0OHDjpx4oRXigIAAPClBn8Ftnr1akmnr/pavny5qqurXdtatWqlvn376quvvvJ+hQAAAF7W4AB07NgxSafPAJWXl7vdBdrpdCozM1PLli3zfoUAAABe1uAANHLkSEmn7/j86quvqqKiwmdFAQAA+JLHV4HNnTvXF3UAAAA0Go8DkCTdc889iouLU1RUlIKDg922DRgwwCuFAQAA+IrHV4GNGzdOycnJcjgcuuaaa7Rjxw79/PPPuvTSS7VhwwZf1AgAAOBVHgegMWPGaNSoUXr66afldDo1f/583XrrrVq0aJEuuugiX9QIAADgVR4HoKioKNfl7pWVlbJarZKkf/zjHzwLDAAANAseB6CioiLXnaB/+uknXX/99ZKkbt26yWKxeLc6AAAAH/A4AG3evFl//vOfJUnJycl67bXXlJaWppUrV2rNmjVeLxAAAMDbPL4KbNSoUQoIOJ2bli5dqp9//lk33HCD1q1bp7///e9eLxAAAMDbPA5AhmHo1KlTrtcrV67UypUrJUmdO3dWQUGB96oDAADwgXO6D9Av2Ww2Pfvss3r88ccVGhrqjSnRkr07yd8V+NfIBf6uAABMr8FrgMLCwvTBBx+ouLhY+fn5GjdunCwWixISErR//34NHDhQjz32mC9rBQAA8IoGnwF66aWXdMMNN2j58uW67bbb9Nprryk2Nla1tbUaOnSotm/f7ss6AQAAvKbBZ4CGDRumxx57TJMnT9add94pi8WirKws3XnnnecdfsaMGSO73a7KykplZmZq4MCBZx37xBNPaOvWrSopKVFJSYk2bdpUZ3xycrIMw3Br3KUaAACc0eAA1LlzZ+Xk5EiSDh48qKqqKq1YseK8C4iLi1NSUpISEhLUv39/ZWdnKzU1VeHh4fWOHzx4sD788EMNGTJE0dHROnTokNLS0tS5c2e3cRs2bFBERISrcZNGAABwRoMDkMVi0cmTJ12vT506pcrKyvMuYOLEiVq2bJmWL1+unJwcxcfHq6KiQiNHjqx3/MMPP6w333xT2dnZ+v777/XEE08oICBAMTExbuOqq6vlcDhcrbS09LxrBQAALUOD1wBZLBb97//+rysEXXDBBVq/fr2cTqfbOE+eBh8UFKQBAwYoMTHR1WcYhtLT0xUdHd2gOS688EIFBQWppKTErX/w4MFyOBw6evSoNm/erJkzZ9YZc0ZwcLBCQkJcr8883gMAALRMDQ5ACQkJbq8/+eST8/7wjh07KjAwUA6Hw63f4XCoZ8+eDZrj5ZdfVkFBgdLT0119Gzdu1Mcffyy73a7LLrtML774ojZs2KDo6GjV1tbWmWP69OmaM2fOee0LAABoPhocgObOnevLOs7J1KlTdf/992vw4MGqrq529Z+5MaMkffPNN9qzZ4/279+vwYMHa/PmzXXmSUxMVFJSkuu11WpVfn6+b4sHAAB+4/GzwLzpyJEjOnnypGw2m1u/zWZTUVHRr7530qRJmjZtmm699Vbt3bv3V8fa7XYVFxere/fu9W53Op0qLy93awAAoOXyawCqqanR7t273RYwWywWxcTEKCMj46zvmzx5sp577jnFxsZq9+7dv/k5kZGR6tChgwoLC71SNwAAaN78GoAkKSkpSU8++aSGDx+unj176s0331RoaKiSk5MlSSkpKXrxxRdd46dMmaLnn39eI0eO1IEDB2Sz2WSz2VyP4AgNDdX8+fM1aNAgde3aVUOHDtUnn3yiffv2KTU11S/7CAAAmhavPAvsfKxatUrh4eGaO3euIiIilJWVpdjYWB0+fFiSFBUV5bZwefTo0QoJCdHq1avd5pkzZ44SEhJ06tQp9e3bVyNGjFBYWJgKCgqUlpam5557rs4VawAAwJz8HoAkacmSJVqyZEm924YMGeL2ulu3br86V1VVlWJjY71WGwAAaHnOKQANHTpUMTEx6tSpkwIC3L9Fe/zxx71SGAAAgK94HIBmzZqlWbNmadeuXSosLJRhGL6oCwAAwGc8DkDx8fF69NFHvfIcMAAAAH/w+Cqw4OBgffXVV76oBQAAoFF4HID+53/+Rw8++KAvagEAAGgUHn8F1rp1a40aNUp/+MMftGfPHtXU1LhtnzRpkteKAwAA8AWPA1Dfvn2VlZUlSerdu7fbNhZEAwCA5sDjADR06FBf1AEAANBozutRGJGRkYqMjPRWLQAAAI3C4wBksVj03HPPqbS0VAcPHtTBgwd19OhRzZw5UxaLxRc1AgAAeJXHX4HNmzdPjz/+uKZNm6Zt27ZJkm666SbNmTNHrVu31syZM71eJAAAgDd5HIBGjBihJ554QuvXr3f17d27V/n5+Vq6dCkBCAAANHkefwXWvn175ebm1unPzc1V+/btvVIUAACAL3kcgLKzszV27Ng6/WPHjlV2drZXigIAAPAlj78CmzJlij799FP94Q9/UEZGhiQpOjpal1xyiW6//XavFwgAAOBtHp8B2rp1q6644gqtWbNGYWFhCgsL08cff6wePXroyy+/9EWNAAAAXuXxGSBJKiwsZLEzAABothoUgPr06aNvvvlGhmGoT58+vzp27969XikMAADAVxoUgLKyshQREaHi4mJlZWXJMIx6b3poGIYCA8/ppBIAAECjaVBa6datm4qLi11/BgAAaM4aFIB++ukn15+7du2qr776SqdOnXIb06pVK91www1uYwEAAJoij68C27JlS703PLzooou0ZcsWrxQFAADgS+f0MFTDMOr0d+jQQSdOnPBKUQAAAL7U4BXLq1evlnR6ofPy5ctVXV3t2taqVSv17dtXX331lfcrBAAA8LIGB6Bjx45JOn0GqLy8XJWVla5tTqdTmZmZWrZsmfcrBAAA8LIGB6CRI0dKkg4cOKBXX31VFRUVPisKAADAlzxeAzR37lxVV1crJiZGo0aNUps2bSRJF198sUJDQ71eIAAAgLd5fNfCqKgobdy4UVFRUQoJCdGmTZt0/PhxTZ06VSEhIRo9erQv6gQAAPAaj88ALVy4ULt27VK7du3c1gGtWbNGMTExXi0OAADAFzw+A3TzzTfrhhtuUE1NjVv/gQMHFBkZ6bXCAAAAfMXjM0ABAQFq1apVnf4uXbqovLzcK0UBAAD4kscBKC0tTRMmTHC9NgxDoaGhSkhI0GeffebN2gAAAHzC46/AJk2apNTUVH377bdq3bq1PvjgA11++eU6cuSIHnjgAV/UCAAA4FUeB6D8/Hz169dP999/v/r27as2bdronXfe0fvvv6+qqipf1AgAAOBVHgcgSTp16pTef/99vf/++96uBwAAwOcaFIDuvPPOBk+4fv16j4sYM2aMJk+erIiICGVnZ2vcuHHauXNnvWOfeOIJDR8+XL1795Yk7d69WzNmzKgzPiEhQU8++aTCwsK0bds2jR49Wvv27fO4NgAA0PI0KACtXbu2QZMZhqHAQM9OKsXFxSkpKUnx8fHavn27JkyYoNTUVPXo0UPFxcV1xg8ePFgffvihvvrqK1VVVWnq1KlKS0vTVVddpYKCAknSlClT9PTTT2vEiBGy2+16/vnnlZqaqiuvvNLtIa4AAMCcLJIMfxaQmZmpnTt3aty4cacLslh06NAhLV68WC+//PJvvj8gIEBHjx7V2LFj9Y9//EOSVFBQoAULFmjBggWSpLZt28rhcOjRRx/VypUrf3NOq9WqsrIytW3blkv7feHdSf6uwL9GLvB3BeAY9HcFgE948vvb48vgvSkoKEgDBgxQenq6q88wDKWnpys6OrpBc1x44YUKCgpSSUmJJKlbt266+OKL3eYsKyvT9u3bGzwnAABo2c4pAA0dOlTr16/Xvn37tG/fPq1fv/6cHoPRsWNHBQYGyuFwuPU7HA5FREQ0aI6XX35ZBQUFrsBz5n2ezBkcHCyr1erWAABAy+XxVWCjR4/WwoUL9dFHH2nhwoWSpOuvv16fffaZnnnmGS1dutTrRZ7N1KlTdf/992vw4MHntbZn+vTpmjNnjvcKA5q6iev8XYGffeHvAgD4mcdngGbMmKFnnnlGDz74oBYvXqzFixfroYce0jPPPKMZM2Z4NNeRI0d08uRJ2Ww2t36bzaaioqJffe+kSZM0bdo03Xrrrdq7d6+r/8z7PJkzMTFRbdu2dTWeaQYAQMvm8RmgsLAwbdy4sU5/WlpagxYt/7eamhrt3r1bMTEx+uSTTySdXgQdExOjN95446zvmzx5sp599lnddttt2r17t9s2u92uwsJCxcTEKDs7W9LpRVGDBg3Sm2++We98TqdTTqfTo9oBADhnLMT3dwWenwFat26d/vKXv9Tpv+uuu/TPf/7T4wKSkpL05JNPavjw4erZs6fefPNNhYaGKjk5WZKUkpKiF1980TV+ypQpev755zVy5EgdOHBANptNNptNoaGhrjGvv/66Zs6cqTvvvFO9e/fWe++9p4KCggZfzg8AAFo2j88Afffdd3r22Wc1ePBgZWRkSDq9BujGG2/UggULXJezS9LixYt/c75Vq1YpPDxcc+fOVUREhLKyshQbG6vDhw9LkqKiolRbW+saP3r0aIWEhGj16tVu88yZM0cJCQmSpPnz5ys0NFRvv/22wsLC9OWXXyo2NpZ7AAEAAEnncB+g/fv3N2icYRi67LLLzqUmv+M+QD7GqV9/V8Ai6N4mXwTdFI5Bs+P/gz6Z1pPf3x6fAbr00kvPuTAAAICmwK83QgQAAPCHc3oa/L333qshQ4aoU6dOCghwz1D33HOPVwoDAADwFY8D0Ouvv66//e1v2rJlixwOhwzDr48SAwAA8JjHAeiRRx7RX//6V23YsMEX9QAAAPicx2uAjh071uArwQAAAJoijwPQnDlzNHv2bLVu3doX9QAAAPicx1+BrVq1Sg888IAOHz6sAwcOqKamxm37gAEDvFYcAACAL3gcgFJSUjRgwACtWLGCRdDnyuw3oeNJ3AAAP/M4AN1xxx267bbbtG3bNl/UAwAA4HMerwE6dOiQysrKfFELAABAo/A4AE2aNEnz589X165dfVEPAACAz3n8FdiKFSt04YUXKi8vTxUVFXUWQXfo0MFrxQEAAPiCxwFowoQJPigDAACg8XgcgN577z1f1AEAANBozulhqAEBAbr77rvVq1cvSdK3336rdevWqba21qvFAQBaKG4H4u8CTM/jAHTZZZfps88+U2RkpL7//ntJ0vTp03Xo0CHdcccdPCYDAAA0eR5fBbZo0SLl5eXpkksu0YABAzRgwABFRUXJbrdr0aJFvqgRAADAqzw+A3TLLbfo+uuv19GjR119JSUlmjZtGjdHBAAAzYLHZ4Cqq6tltVrr9Ldp00ZOp9MrRQEAAPiSxwHon//8p95++21dd911rr5Bgwbprbfe0rp1Zl/UBgAAmgOPA9DTTz+tvLw8ZWRkqKqqSlVVVdq2bZv27dun8ePH+6JGAAAAr/J4DdCxY8d0991367LLLnNdBp+Tk6O8vDyvFwcAAOALHgUgq9Wq48ePyzAM5eXluUKPxWKR1WpVeXm5T4oEAADwpgZ/BXb33Xdr165dat26dZ1tF1xwgXbu3Kk//elPXi0OAADAFxocgEaPHq358+ersrKyzraKigq9/PLLGjt2rFeLAwAA8IUGB6DevXvr888/P+v2rVu3qk+fPt6oCQAAwKcaHIDatWunwMCzLxkKCgpSu3btvFIUAACALzU4AB04cEDXXnvtWbdfe+21OnjwoFeKAgAA8KUGB6CPP/5Y8+bNU6dOnepss9lseuGFF7R69WqvFgcAAOALDb4M/qWXXtJdd92lH3/8UStWrHA9Cb5nz5566KGHdOjQIb300ks+KxQAAMBbGhyAjh8/rhtvvFGJiYm67777XOt9SktLtWLFCj377LM6fvy4zwoFAADwFo9uhFhWVqannnpKTz31lDp27CiLxaLi4mJf1QYAAOATHj8K44wjR454sw4AAIBG4/HDUAEAAJo7AhAAADAdvwegMWPGyG63q7KyUpmZmRo4cOBZx1555ZX66KOPZLfbZRiGxo8fX2fM7NmzZRiGW8vJyfHlLgAAgGbG4wAUGRl51m2DBg3yaK64uDglJSUpISFB/fv3V3Z2tlJTUxUeHl7v+AsvvFD79+/XtGnTVFhYeNZ5v/nmG0VERLjaTTfd5FFdAACgZfM4AKWlpdX7yIsbbrhBGzdu9GiuiRMnatmyZVq+fLlycnIUHx+viooKjRw5st7xu3bt0pQpU7Ry5UpVV1efdd6TJ0/K4XC42s8//+xRXQAAoGXzOABlZmYqLS1Nbdq0cfXdfPPN+uyzz5SQkNDgeYKCgjRgwAClp6e7+gzDUHp6uqKjoz0ty83ll1+u/Px85eXlacWKFbrkkkt+dXxwcLCsVqtbAwAALZfHAeiJJ57QTz/9pPXr1ys4OFiDBw/Wp59+qlmzZun1119v8DwdO3ZUYGCgHA6HW7/D4VBERISnZbls375djz76qGJjYzV69Gh169ZN//rXv9wC2y9Nnz5dZWVlrpafn3/Onw8AAJo+jwOQYRi6//77VVNTo82bN2vdunWaPn26Fi1a5Iv6PLZx40Z99NFH2rt3r9LS0nT77bcrLCxMcXFxZ31PYmKi2rZt62q/ts4JAAA0fw26EWKfPn3q9M2ZM0cffvihVqxYoa1bt7rG7N27t0EffOTIEZ08eVI2m82t32azqaioqEFzNMSxY8f0ww8/qHv37mcd43Q65XQ6vfaZAACgaWtQAMrKypJhGLJYLK6+M6//9re/adSoUbJYLDIMQ4GBDbu5dE1NjXbv3q2YmBh98sknkiSLxaKYmBi98cYb57Ar9QsNDdVll12mf/zjH16bEwAANG8NSivdunXzyYcnJSUpJSVFu3bt0o4dOzRhwgSFhoYqOTlZkpSSkqL8/HzNmDFD0umF01deeaWk0wuXIyMj1a9fPx0/flx5eXmSpFdeeUXr16/XwYMH1blzZyUkJOjUqVP68MMPfbIPAACg+WlQAPrpp5988uGrVq1SeHi45s6dq4iICGVlZSk2NlaHDx+WJEVFRam2ttY1vnPnzsrKynK9njx5siZPnqzPP/9cQ4YMkSR16dJFH374oTp06KDi4mJ9+eWXuv7663l2GQAAcPH4YajTpk2Tw+FwnaU547HHHlN4eLjmz5/v0XxLlizRkiVL6t12JtSccfDgQbev4erzwAMPePT5AADAfDy+Cuxvf/ubcnNz6/R/++23io+P90pRAAAAvuRxAIqIiKj3MRTFxcW6+OKLvVIUAACAL3kcgA4dOqQbb7yxTv+NN96ogoICrxQFAADgSx6vAVq2bJlef/11BQUFafPmzZKkmJgYzZ8/XwsWLPB6gQAAAN7mcQB65ZVX1KFDBy1dulTBwcGSpKqqKr388st66aWXvF4gAACAt3kcgKTTV4I9//zz6tWrlyorK/Xjjz9yJ2UAANBsnFMAkqQTJ064FkMTfgAAQHPi8SJoi8Wi5557TqWlpTp48KAOHjyoo0ePaubMmb95jx4AAICmwOMzQPPmzdPjjz+uadOmadu2bZKkm266SXPmzFHr1q01c+ZMrxcJAADgTR4HoBEjRuiJJ57Q+vXrXX179+5Vfn6+li5dSgACAABNnsdfgbVv377eO0Hn5uaqffv2XikKAADAlzwOQNnZ2Ro7dmyd/rFjxyo7O9srRQEAAPiSx1+BTZkyRZ9++qn+8Ic/KCMjQ5IUHR2tSy65RLfffrvXCwQAAPA2j88Abd26VVdccYXWrFmjsLAwhYWF6eOPP1aPHj305Zdf+qJGAAAArzqn+wAVFhay2BkAADRbDQpAffr0afCEe/fuPediAAAAGkODAlBWVpYMw/jNGx0ahqHAwHO+uTQAAECjaFBa6datm6/rAAAAaDQNCkA//fSTr+sAAABoNB5/X9W+fXuVlJRIkrp06aInn3xSF1xwgdatW8dVYAAAoFlo8GXwvXv3lt1u1+HDh5WTk6N+/fpp586deuaZZzRq1Cht2bJFd911ly9rBQAA8IoGB6D58+dr7969+v3vf6/PP/9c//znP/Xpp5/qoosuUrt27fT3v/9d06ZN82WtAAAAXtHgr8AGDhyooUOHau/evcrOztaoUaO0dOlSGYYhSVq8eLEyMzN9VigAAIC3NPgMUPv27VVUVCRJOnHihE6cOKGjR4+6th89elRWq9X7FQIAAHiZR4/COHO252yvAQAAmgOPrgJbvny5qqurJUmtW7fWW2+9pRMnTkiSQkJCvF8dAACADzQ4AKWkpLi9XrFiRZ0x77333vlXBAAA4GMNDkAjR470ZR0AAACNxqM1QAAAAC0BAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJiO3wPQmDFjZLfbVVlZqczMTA0cOPCsY6+88kp99NFHstvtMgxD48ePP+85AQCA+fg1AMXFxSkpKUkJCQnq37+/srOzlZqaqvDw8HrHX3jhhdq/f7+mTZumwsJCr8wJAADMx68BaOLEiVq2bJmWL1+unJwcxcfHq6Ki4qx3nd61a5emTJmilStXup5Jdr5zAgAA8/FbAAoKCtKAAQOUnp7u6jMMQ+np6YqOjm7UOYODg2W1Wt0aAABoufwWgDp27KjAwEA5HA63fofDoYiIiEadc/r06SorK3O1/Pz8c/p8AADQPPh9EXRTkJiYqLZt27paZGSkv0sCAAA+1OCnwXvbkSNHdPLkSdlsNrd+m82moqKiRp3T6XTK6XSe02cCAIDmx29ngGpqarR7927FxMS4+iwWi2JiYpSRkdFk5gQAAC2P384ASVJSUpJSUlK0a9cu7dixQxMmTFBoaKiSk5MlSSkpKcrPz9eMGTMknV7kfOWVV0o6vXA5MjJS/fr10/Hjx5WXl9egOQEAAPwagFatWqXw8HDNnTtXERERysrKUmxsrA4fPixJioqKUm1trWt8586dlZWV5Xo9efJkTZ48WZ9//rmGDBnSoDkBAAD8GoAkacmSJVqyZEm9286EmjMOHjwoi8VyXnMCAABwFRgAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADCdJhGAxowZI7vdrsrKSmVmZmrgwIG/Ov7ee+9VTk6OKisrtWfPHg0bNsxte3JysgzDcGsbNmzw5S4AAIBmxO8BKC4uTklJSUpISFD//v2VnZ2t1NRUhYeH1zs+OjpaH374od555x1dc801Wrt2rdauXaurrrrKbdyGDRsUERHhag888EBj7A4AAGgG/B6AJk6cqGXLlmn58uXKyclRfHy8KioqNHLkyHrHjx8/Xhs3btSrr76q3NxczZo1S//+9781duxYt3HV1dVyOByuVlpa2gh7AwAAmgO/BqCgoCANGDBA6enprj7DMJSenq7o6Oh63xMdHe02XpJSU1PrjB88eLAcDodyc3O1dOlStW/f/qx1BAcHy2q1ujUAANBy+TUAdezYUYGBgXI4HG79DodDERER9b4nIiLiN8dv3LhRw4cPV0xMjKZOnapbbrlFGzZsUEBA/bs7ffp0lZWVuVp+fv557hkAAGjKAv1dgC+sXLnS9edvvvlGe/bs0f79+zV48GBt3ry5zvjExEQlJSW5XlutVkIQAAAtmF/PAB05ckQnT56UzWZz67fZbCoqKqr3PUVFRR6NlyS73a7i4mJ179693u1Op1Pl5eVuDQAAtFx+DUA1NTXavXu3YmJiXH0Wi0UxMTHKyMio9z0ZGRlu4yXpj3/841nHS1JkZKQ6dOigwsJC7xQOAACaNb9fBZaUlKQnn3xSw4cPV8+ePfXmm28qNDRUycnJkqSUlBS9+OKLrvELFy5UbGysJk6cqB49emj27Nm69tpr9cYbb0iSQkNDNX/+fA0aNEhdu3bV0KFD9cknn2jfvn1KTU31yz4CAICmxe9rgFatWqXw8HDNnTtXERERysrKUmxsrA4fPixJioqKUm1trWt8RkaGHnzwQb3wwgt68cUX9eOPP+ruu+/Wt99+K0k6deqU+vbtqxEjRigsLEwFBQVKS0vTc889J6fT6Zd9BAAATYvfA5AkLVmyREuWLKl325AhQ+r0ffTRR/roo4/qHV9VVaXY2Fiv1gcAAFoWv38FBgAA0NgIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHSaRAAaM2aM7Ha7KisrlZmZqYEDB/7q+HvvvVc5OTmqrKzUnj17NGzYsDpjEhISVFBQoIqKCm3atEndu3f3VfkAAKCZ8XsAiouLU1JSkhISEtS/f39lZ2crNTVV4eHh9Y6Pjo7Whx9+qHfeeUfXXHON1q5dq7Vr1+qqq65yjZkyZYqefvppxcfHa9CgQTpx4oRSU1MVEhLSWLsFAACaML8HoIkTJ2rZsmVavny5cnJyFB8fr4qKCo0cObLe8ePHj9fGjRv16quvKjc3V7NmzdK///1vjR071jVmwoQJeuGFF7Ru3Trt3btXw4cPV+fOnXX33Xc30l4BAICmLNCfHx4UFKQBAwYoMTHR1WcYhtLT0xUdHV3ve6Kjo5WUlOTWl5qa6go33bp108UXX6z09HTX9rKyMm3fvl3R0dFauXJlnTmDg4Pdzg5ZrVa3f3pdiF9/7P4XZPIzcb46rjzBMejvCvyLY9D/OAZ9NG3D5/XrEdixY0cFBgbK4XC49TscDvXs2bPe90RERNQ7PiIiwrX9TN/ZxvzS9OnTNWfOnDr9+fn5DdoPeKrumi1TeXievysAx6C/KwDHoE+nt1qtKi8v/9UxJo/gpyUmJtY5q9S+fXuVlJT4qSL4itVqVX5+viIjI3/zPw7AFzgG4W8t/Ri0Wq0qKCj4zXF+DUBHjhzRyZMnZbPZ3PptNpuKiorqfU9RUdGvjj/zz1/OYbPZlJWVVe+cTqdTTqfTra8lHhT4P+Xl5fw7hl9xDMLfWuox2NB98usi6JqaGu3evVsxMTGuPovFopiYGGVkZNT7noyMDLfxkvTHP/7RNd5ut6uwsNBtjNVq1aBBg846JwAAMB/Dny0uLs6orKw0hg8fbvTs2dN46623jJKSEqNTp06GJCMlJcV48cUXXeOjo6MNp9NpTJw40ejRo4cxe/Zso7q62rjqqqtcY6ZMmWKUlJQYd955p9G7d29jzZo1Rl5enhESEuLXfaX5v1mtVsMwDMNqtfq9Fpo5G8cgzd+NY9DV/F6A8dRTTxkHDhwwqqqqjMzMTOO6665zbduyZYuRnJzsNv7ee+81cnNzjaqqKmPv3r3GsGHD6syZkJBgFBYWGpWVlcamTZuMyy+/3O/7SfN/Cw4ONmbPnm0EBwf7vRaaORvHIM3fjWPwdLP8/z8AAACYht9vhAgAANDYCEAAAMB0CEAAAMB0CEBAAxmGobvuusvfZaCZ6dq1qwzDUL9+/fxdCkyKY7B+BCA0O4Zh/GqbPXv2Wd/L/wjQ2A4dOqSIiAh988035zVPnz59tHXrVlVWVuqnn37S5MmTvVQhWjpvHIMhISFKTk7Wnj17VFNTozVr1nixQv/gURhodv77mW733Xef5s6dqx49erj6jh8/7o+ygDqCgoJUU1NT59mEnrJarUpLS1N6erri4+PVp08fvfvuuyotLdWyZcu8VC1aIm8dg61atVJlZaUWLVqke+65x0vV+Z/fr8Wn0c61jRgxwjh69KjrtcViMZ577jnj0KFDRlVVlfH1118bt912m2v7L23ZssWQZFx77bVGWlqaUVxcbJSWlhqff/65cc0117h9lmEYxl133eX3fab5p23ZssVYvHixsXjxYqO0tNQoLi425s6d6zbGbrcbM2fONFJSUoxjx44ZycnJRteuXQ3DMIx+/fq5xv3+9783tm/fblRVVRkFBQVGYmKi0apVq7N+dnx8vPHzzz8bQUFBrr7ExEQjJyfH7z8XWuM1fx6D/92Sk5ONNWvW+P3n4YXm9wJotHNuvwxAEyZMMEpLS4377rvPuOKKK4yXXnrJqK6uNrp3725Ip4OOYRjG0KFDDZvNZrRr186QZAwZMsR46KGHjB49ehg9e/Y0li1bZhQWFhpt2rRxzU0AMnfbsmWLUVZWZrz22mvGFVdcYTz44IPG8ePHjSeeeMI1xm63G6WlpcbEiRONSy+91Lj00kvr/PLp3Lmzcfz4ceONN94wevToYdx1113G4cOHjdmzZ5/1s1NSUur8whk8eLBhGIYRFhbm958NrXGaP4/B/24EIBqtCbRfBqD//Oc/xvTp093GbN++3XjjjTcMSfX+Tai+ZrFYjGPHjhl33HGHq48AZO62ZcsW49tvv3XrS0xMdOuz2+3Gxx9/7Dbml8fcCy+8UOfMzejRo42ysjLDYrHU+9mpqanGW2+95dbXq1cvwzAMo2fPnn7/2dAap/nzGPzv1lICEIug0WJYrVZFRkZq27Ztbv3btm1Tr169fvW9nTp10ttvv60ffvhBpaWlKisrU5s2bRQVFeXLktHMZGZmur3OyMjQ5ZdfroCA//tf6a5du351jl69etV5MPO2bdtktVrVpUsX7xWLFolj0HtYBA1ISklJUYcOHTR+/HgdPHhQ1dXVysjIUHBwsL9LQzNz4sQJr89ZVFQkm83m1nfmdVFRkdc/D82bL47BlogzQGgxysvLlZ+frxtvvNGt/8Ybb9R3330nSXI6nZJOX9HwyzGLFi3Shg0b9N1336m6ulrh4eGNUziajUGDBrm9vv766/Xjjz+qtra2wXPk5OQoOjrare/GG29UWVmZ/vOf/9T7noyMDP3+979XYOD//Z31j3/8o3Jzc1VaWtrwHUCz569jsCUiAKFFeeWVVzR16lTFxcXpiiuuUGJioq6++motXLhQknT48GFVVFQoNjZWnTp1Utu2bSVJP/74ox555BH17NlT1113nd5//31VVFT4c1fQBEVFRWnBggW64oordP/992vcuHGuY6uhli5dqksuuUSLFy9Wjx499Oc//1kJCQlKSkqSYRj1vueDDz6Q0+nUO++8oyuvvFJxcXEaP368kpKSvLFbaEb8dQxKp78669evn9q3b6+LLrpI/fr1a/b3VPP7QiQa7VxbfZfBz5o1yzh06JBRXV1d5zJ4Scbjjz9uHDx40Dh58qTrMvirr77a2LFjh1FRUWF8//33xj333GPY7XZj/PjxrvexCNrcbcuWLcYbb7xhLF261CgtLTV+/vln44UXXnAb88tjRqp/4f25XILcp08fY+vWrUZlZaVx6NAhY8qUKX7/mdAat/n7GLTb7XVuJWKcTkzNsln+/x8AAL9iy5YtysrK0jPPPOPvUmBSHIPexVdgAADAdAhAAADAdPgKDAAAmA5ngAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOn8P1OmdrwzpXAfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_table_stats([n_n_file, t_n_file], labels=[\"QUIC\", \"DTP\"])\n",
    "draw_cmp_fig([n_n_file, t_n_file], trace_file, [\"QUIC\", \"DTP\"], width=0.4, save=True, save_name=[\"new_imgs/bcr.svg\", \"new_imgs/bct.svg\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tos on/off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats of ['25data/client_25_30000_1_n_n.csv', '25data/client_25_30000_1_n_t.csv']\n",
      "|\t| NO DS | DS |\n",
      "| Throughput (Mbps) | 28.79 | 41.16 |\n",
      "| Goodput (Mbps) | 5.31 | 40.60 |\n",
      "| 平均块完成时间 (ms) | 10562 | 2664 |\n",
      "| 高优先级块平均完成时间 (ms) | 10333 | 2620 |\n",
      "| 低优先级块平均完成时间 (ms) | 10676 | 2685 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_table_stats([n_n_file, n_t_file], labels=[\"NO DS\", \"DS\"])\n",
    "draw_cmp_fig([n_n_file, n_t_file], trace_file, [\"NO DS\", \"DS\"], width=0.4, save=True, save_name=[\"new_imgs/ds_bcr.svg\", \"new_imgs/ds_bct.svg\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## final stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "stats of 25data/client_25_30000_1_n_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 10333.634\n",
      "prio 1 avg_bcr 0.2\n",
      "------------\n",
      "prio 2 avg_bct 10676.9095\n",
      "prio 2 avg_bcr 0.1765\n",
      "-------------------------\n",
      "ideal throughput:  11250000.00000031 Mbps\n",
      "throughput: 28.793262376603874 Mbps\n",
      "goodput:  5.307558031420648 Mbps\n",
      "avg_bcr(block completion rate): 0.18825\n",
      "avg_bct(block completion time): 10562.484333333334\n",
      "\n",
      "===============================\n",
      "stats of 25data/client_25_30000_1_n_t.csv\n",
      "------------\n",
      "prio 1 avg_bct 2620.65\n",
      "prio 1 avg_bcr 0.355\n",
      "------------\n",
      "prio 2 avg_bct 2685.5837912087914\n",
      "prio 2 avg_bcr 0.359\n",
      "-------------------------\n",
      "ideal throughput:  11250000.00000031 Mbps\n",
      "throughput: 41.162506618837114 Mbps\n",
      "goodput:  40.595008825378876 Mbps\n",
      "avg_bcr(block completion rate): 0.357\n",
      "avg_bct(block completion time): 2664.098345588235\n",
      "\n",
      "===============================\n",
      "stats of 25data/client_25_30000_1_t_n.csv\n",
      "------------\n",
      "prio 1 avg_bct 4163.215568862275\n",
      "prio 1 avg_bcr 0.379\n",
      "------------\n",
      "prio 2 avg_bct 4857.480456026059\n",
      "prio 2 avg_bcr 0.1975\n",
      "-------------------------\n",
      "ideal throughput:  11250000.00000031 Mbps\n",
      "throughput: 24.064631340819027 Mbps\n",
      "goodput:  16.704954850039396 Mbps\n",
      "avg_bcr(block completion rate): 0.28825\n",
      "avg_bct(block completion time): 4545.528251121076\n",
      "\n",
      "===============================\n",
      "stats of 25data/client_25_30000_1_t_t.csv\n",
      "------------\n",
      "prio 1 avg_bct 2338.5407407407406\n",
      "prio 1 avg_bcr 0.54\n",
      "------------\n",
      "prio 2 avg_bct 3538.98202247191\n",
      "prio 2 avg_bcr 0.439\n",
      "-------------------------\n",
      "ideal throughput:  11250000.00000031 Mbps\n",
      "throughput: 45.273753847180764 Mbps\n",
      "goodput:  44.893834234477154 Mbps\n",
      "avg_bcr(block completion rate): 0.48950000000000005\n",
      "avg_bct(block completion time): 3085.6685314685315\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_stats(n_n_file, trace_file)\n",
    "print()\n",
    "get_stats(n_t_file, trace_file)\n",
    "print()\n",
    "get_stats(t_n_file, trace_file)\n",
    "print()\n",
    "get_stats(t_t_file, trace_file)\n",
    "draw_cmp_fig([n_n_file, n_t_file, t_n_file, t_t_file], trace_file, labels=[\"QUIC\", \"DTP\", \"QUIC + DS\", \"DTP + DS\"], width=0.2, save=True, save_name=[\"new_imgs/total_bcr.svg\", \"new_imgs/total_bct.svg\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playground"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WlqbNmzdr06ZNGj9+vDp06KDU1FRJUlpamvLz8zVp0iRJ0pQpU7Rx40Z999136ty5s5566in16NFDf//73335NQAAQAvh83CzZMkShYSEKCkpSWFhYdq2bZuGDx/uur07IiJCJ0+edI0/77zztGDBAoWFhenYsWPasmWLfvWrXyk7O9tXXwEAALQgDknG10U0J6fTqdLSUnXs2FFlZWW+LgeAzWxOfNfXJeia5Pt9XQLgcVZ+f7e6u6UAAAB+CuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYis9fnAl4Eu/1AQBw5AYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANhKgK8LAAAAnrU58V2frv+a5Pt9un6O3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFtpUrgZPHiw3n77ba1fv17h4eGSpNGjR2vQoEFNKmLs2LHKzc1VZWWlNm7cqAEDBrg137333itjjJYtW9ak9QIAAPuxHG7uvvtuZWRkqLKyUldffbWCgoIkSZ06ddKkSZMsFxAXF6eUlBTNmDFD/fr1U1ZWljIyMhQSEvKT8/Xo0UOzZs3S2rVrLa8TAADYl+VwM3nyZCUkJOjRRx9VbW2tq3/dunXq16+f5QImTJigBQsWaNGiRcrOzlZCQoIqKioUHx9/5qL9/LR48WJNmzZNe/bssbxOAABgX5bDTVRUVKNHS0pKStS5c2dLy2rXrp369++vzMxMV58xRpmZmYqJiTnjfFOnTtWhQ4f05ptvWlofAACwvwCrMxQUFCgyMlL79u2r1z948GDLR1G6dOmigIAAFRYW1usvLCxUdHR0o/MMGjRIv//973XVVVe5tY7AwEDXqTNJcjqdlmoEAACti+UjNwsWLNDs2bM1cOBAGWMUHh6ukSNHatasWXr99de9UaNLcHCw3n77bT3yyCM6evSoW/MkJiaqtLTU1fLz871aIwAA8C3LR25efPFF+fn56dNPP1X79u21du1aVVdXa9asWXrttdcsLevIkSOqq6tTaGhovf7Q0FAVFBQ0GN+rVy/17NlTH374oavPz+9UPqutrVVUVFSDo0fJyclKSUlxfXY6nQQcAABszHK4kaQXXnhBL730kiIjIxUcHKxvvvlGx48ft7yc2tpabdmyRbGxsVq+fLkkyeFwKDY2ttGgtHPnTl1xxRX1+mbOnCmn06lx48YpLy+vwTw1NTWqqamxXBsAAGidLJ+WWrhwoYKDg1VbW6vs7Gx9+eWXOn78uNq3b6+FCxdaLiAlJUWPPPKIHnjgAUVHR+v1119Xhw4dlJqaKklKS0vTCy+8IEmqrq7Wjh076rXi4mKVlZVpx44d9e7eAgAAbZPlcDNmzBide+65DfrPPfdcPfDAA5YLWLJkif785z8rKSlJ27Zt01VXXaXhw4fr0KFDkqSIiAhdcMEFlpcLAADaJrdPSzmdTjkcDjkcDjmdTlVVVbmm+fv769Zbb3UFEqvmzp2ruXPnNjptyJAhPznvQw891KR1AgAAe3I73BQXF8sYI2OMvv322wbTjTGaNm2aR4sDAACwyu1wM2TIEDkcDn322WcaMWKEioqKXNNqamq0b98+HTx40CtFAgAAuMvtcHP6qcQ9e/ZUXl6ejDFeKwoAAKCpLN8Kvn//fkmnLiCOiIhQYGBgvenbt2/3TGUAAABNYDncdOnSRampqbrlllsaX2BAkx6dAwAA4BGWbwV/5ZVX1LlzZ1177bWqrKzU8OHDNWbMGO3evVt33HGHN2oEAABwm+XDLEOHDtWdd96pLVu26OTJk9q3b58yMzNVWlqqxMREffTRR96oEwAAwC2Ww02HDh1cz7M5duyYQkJCtHv3bm3fvl39+vXzeIEAYMUO688S9ahK364egJpwWmrXrl2KioqSJGVlZekPf/iDwsPDlZCQwK3gAADA5ywfuZk9e7brdQgzZszQxx9/rFGjRqmmpkYPPvigp+sDAACwxHK4Wbx4sevP//73v9WjRw9FR0dr//79Onr0qEeLAwAAsMryaakfq6ys1NatW1VeXq6JEyd6oiYAAIAmsxRuunTpottuu00333yz/PxOzRoQEKA//vGP2rt3r5599lmvFAkAAOAut09LDRo0SCtXrlTHjh1ljNHmzZv10EMPKT09XXV1dZo+fbrS0tK8WSsAAMDPcvvIzcyZM/XRRx/pyiuvVEpKigYMGKBly5Zp0qRJuvzyy/XGG2+oqqrKm7UCAAD8LLfDTZ8+fTRz5kzt2LFDU6dOlTFGTz/9tJYuXerN+gAAACxxO9ycd955OnLkiCSpqqpKFRUV+vrrr71WGAAAQFNYuhX8sssuU1FRkSTJ4XAoKipKHTp0qDeGt4IDAABfshRuPv30UzkcDtfnlStXSpKMMXI4HDLG8FZwAADgU24nkZ49e3qzDgAAAI9wO9zs37/fm3UAAOAZb/r4gbLxL/t2/bD++gW0cOzUAIA27qxfvwAAANCScOQGgGf5+ujhGo4eAm0dR24AAICtEG4AAICtWA43Xbt21VtvvaX8/HzV1taqrq6uXgMAAPAly9fcLFq0SBEREXruued08OBBGWO8URcAAECTWA43gwcP1nXXXaesrCxv1AMAAHBWLJ+WysvLq/cKBgAAgJbEcrgZP368XnzxRfXo0cMb9QAAAJwVy6el3nvvPbVv3145OTmqqKhQbW1tvem/+MUvPFYcAACAVZbDzfjx471QBgAAgGdYDjdvvfWWN+oAAADwiCa9fsHPz0933XWXevfuLUnasWOHVqxYoZMnT3q0OAAAAKssh5tevXrpo48+Urdu3bRr1y5JUmJiovLy8nTbbbdpz549Hi8SAADAXZbvlpozZ45ycnJ04YUXqn///urfv78iIiKUm5urOXPmeKNGAAAAt1k+cnPDDTfol7/8pY4dO+bqKyoq0rPPPqt169Z5tDgAAACrLB+5qa6ultPpbNAfHBysmpoajxQFAADQVJbDzcqVK/W3v/1NAwcOdPVde+21mj9/vlasWOHR4gAAAKyyHG7++Mc/KicnRxs2bFBVVZWqqqq0bt06fffddxo3bpw3agQAAHCb5WtuSkpKdNdddykyMlLR0dGSpOzsbOXk5Hi8OAAAAKua9JwbSfruu+/03XffebIWAACAs+ZWuHn55Zc1ZcoUVVRU6OWXX/7JsRMnTvRIYQAAAE3hVri5+uqr1a5dO9efAQAAWiq3ws3QoUMb/TMAAEBLY/luqYULFyo4OLhBf/v27bVw4UKPFAUAANBUlsPNmDFjdO655zboP/fcc/XAAw94pCgAAICmcvtuKafTKYfDIYfDIafTqaqqKtc0f39/3XrrrTp06JBXigQAAHCX2+GmuLhYxhgZY/Ttt982mG6M0bRp0zxaHAAAgFVuh5shQ4bI4XDos88+04gRI1RUVOSaVlNTo3379ungwYNeKRIAAMBdboebtWvXSpJ69uyp/fv3e60gAACAs2H5guL9+/dr8ODBevvtt7Vu3TqFh4dLkkaPHq1BgwZ5vEAAAAArLIebu+++WxkZGaqsrFS/fv0UFBQkSerUqZMmTZrk8QIBAACssBxuJk+erISEBD366KOqra119a9bt079+vXzaHEAAABWWQ43UVFRrutvfqikpESdO3duUhFjx45Vbm6uKisrtXHjRg0YMOCMY3/zm9/oyy+/1LFjx1ReXq6tW7dq9OjRTVovAACwH8vhpqCgQJGRkQ36Bw8erD179lguIC4uTikpKZoxY4b69eunrKwsZWRkKCQkpNHxRUVFev755xUTE6Mrr7xSqampSk1N1a9//WvL6wYAAPZjOdwsWLBAs2fP1sCBA2WMUXh4uEaOHKlZs2bp9ddft1zAhAkTtGDBAi1atEjZ2dlKSEhQRUWF4uPjGx3/+eefKz09XTt37tSePXs0Z84cffXVVxo8eLDldQMAAPtx+1bw01588UX5+fnp008/Vfv27bV27VpVV1dr1qxZeu211ywtq127durfv7+Sk5NdfcYYZWZmKiYmxq1lDB06VFFRUXrmmWcanR4YGOi66Fk69aRlAABgX5bDjSS98MILeumllxQZGang4GB98803On78uOXldOnSRQEBASosLKzXX1hYqOjo6DPO17FjR+Xn5ysoKEgnTpzQ2LFjlZmZ2ejYxMRETZ8+3XJtAACgdWpSuJGk2tpaZWdne7IWt5WVlemqq65ScHCwYmNjlZKSoj179ujzzz9vMDY5OVkpKSmuz06nU/n5+c1ZLgAAaEZuhZulS5e6vcARI0a4PfbIkSOqq6tTaGhovf7Q0FAVFBSccT5jjHJyciRJWVlZ6t27txITExsNNzU1NaqpqXG7JgAA0Lq5FW5KSkq8svLa2lpt2bJFsbGxWr58uSTJ4XAoNjbW0vU7fn5+9a6rAQAAbZdb4eZMdy55QkpKitLS0rR582Zt2rRJ48ePV4cOHZSamipJSktLU35+vuvpx88++6w2b96snJwcBQUF6dZbb9Xvfvc7PfbYY16rEQAAtB5NvuYmJCREUVFRkqRdu3bp8OHDTVrOkiVLFBISoqSkJIWFhWnbtm0aPny4Dh06JEmKiIjQyZMnXeM7dOigefPmqXv37qqsrNTOnTs1evRoLVmypKlfBQAA2IjlcON0OjV37lzdd9998vf3lySdOHFC7733nh5//HGVlpZaLmLu3LmaO3duo9OGDBlS7/OUKVM0ZcoUy+sAAABtQ5Me4nfttdfq9ttvV+fOndW5c2fdfvvtuuaaa/TGG294o0YAAAC3WT5yc/vtt2vYsGFat26dq2/VqlV65JFH9PHHH3u0OAAAAKssH7k5evRoo3dPlZSU6NixYx4pCgAAoKksh5uZM2cqJSWl3rNpQkND9dJLL+m5557zaHEAAABWWT4t9dhjjykyMlL79+/X/v37JZ26o6m6ulohISH6wx/+4Brbv39/z1UKAADgBsvhJj093QtlAAAAeIblcJOUlOSNOgAAADyiyQ/xk049UM/Pr/5lO2VlZWdVEAAAwNmwfEHxRRddpJUrV6q8vNx1h9SxY8dUXFzM3VIAAMDnLB+5+cc//iGHw6H4+HgVFhbKGOONugAAAJrEcrjp27ev+vfvr2+//dYb9QAAAJwVy6elvvzyS1144YXeqAUAAOCsWT5y8/DDD2v+/Pnq1q2bvv76a9XW1tabvn37do8VBwAAYJXlcBMSEqJevXopNTXV1WeMkcPhkDFGAQFndQMWAADAWbGcRN58801t3bpV999/PxcUAwCAFsdyuOnRo4fuuOMO5eTkeKMeAACAs2L5guLPPvtMffv29UYtAAAAZ83ykZsPP/xQf/3rX9WnTx9t3769wQXFH374oceKAwAAsMpyuJk/f74kaerUqQ2mcUExAADwNctJxN/f3xt1AAAAeASHWeBROx7w7forfbt6AEALYPmCYkm6/vrrtWLFCu3evVu7d+/W8uXLNXjwYE/XBgAAYJnlcDNq1ChlZmaqoqJCc+bM0Zw5c1RZWalPP/1U999/vzdqBAAAcJvl01L//d//raefflqvvPKKq+/VV1/Vn/70J02ZMkXvvvuuJ+sDAACwxPKRm4svvrjR271XrFihnj17eqQoAACAprIcbvLy8hQbG9ug/6abblJeXp5HigIAAGgqy6elXn75Zc2ZM0dXXXWV1q9fL0kaNGiQHnzwQY0bN87jBQIAAFjRpIf4FRQUaOLEiYqLi5MkZWdn695779WKFSs8XiAAAIAVTXrOTXp6utLT0z1cCgAAwNlz+5qbzp0764knnpDT6WwwrWPHjmecBgAA0JzcDjdPPPGErr/+epWVlTWYVlpaquuuu05PPvmkR4sDAACwyu1wM2LECNdLMxvzxhtv6J577vFIUQAAAE3ldrjp1auXdu/efcbpu3fvVq9evTxSFAAAQFO5HW5OnDih8PDwM04PDw/XyZMnPVIUAABAU7kdbrZu3aq77rrrjNN/85vfaOvWrZ6oCQAAoMncvhX8tdde0z//+U99//33ev31111Hafz8/DR27Fj96U9/0siRI71WKAAAgDvcDjcffPCB/ud//kdz5szR888/rz179kg69a6p4OBgvfTSS1q6dKnXCgUAAHCHpYf4TZ48WcuXL9eoUaMUGRkph8Ohzz//XO+8846+/PJLb9UIAADgNstPKP7yyy8JMgAAoMWy/FZwAACAloxwAwAAbIVwAwAAbIVwAwAAbKVJ4cbf31+xsbF69NFHFRwcLEm64IIL1KFDB48WBwAAYJXlu6UiIiL08ccfKyIiQkFBQfrkk09UXl6uZ555RkFBQXrssce8UScAAIBbLB+5mT17tjZv3qzzzjtPlZWVrv5ly5YpNjbWo8UBAABYZfnIzXXXXadf/epXqq2trde/d+9edevWzWOFAQAANIXlIzd+fn7y9/dv0N+9e3eVlZV5pCgAAICmshxuVq1apfHjx7s+G2PUoUMHzZgxQx999JEnawMAALDM8mmpiRMnKiMjQzt27NA555yjd955R5dccomOHDmi+++/3xs1AgAAuM1yuMnPz1ffvn1133336corr1RwcLAWLlyoxYsXq6qqyhs1AgAAuM1yuJGkEydOaPHixVq8eLGn6wEAADgrTQo3F1xwgQYPHqyuXbvKz6/+ZTuvvvqqRwoDAABoCsvhZsyYMXrjjTdUU1Ojo0ePyhjjmmaMIdwAAACfsny31HPPPaekpCR16tRJPXv21MUXX+xqvXr1alIRY8eOVW5uriorK7Vx40YNGDDgjGMffvhhrV27VkVFRSoqKtInn3zyk+MBAEDbYjnctG/fXv/85z/rHbE5G3FxcUpJSdGMGTPUr18/ZWVlKSMjQyEhIY2Ov/HGG/Xuu+9qyJAhiomJUV5enlatWqXw8HCP1AMAAFo3y+Fm4cKF+u1vf+uxAiZMmKAFCxZo0aJFys7OVkJCgioqKhQfH9/o+NGjR+v1119XVlaWdu3apYcfflh+fn68+gEAAEhqwjU3iYmJWrlypYYPH67t27c3eA3DxIkT3V5Wu3bt1L9/fyUnJ7v6jDHKzMxUTEyMW8to37692rVrp6KiokanBwYGKigoyPXZ6XS6XR8AAGh9mhRuhg0bpl27dklSgwuKrejSpYsCAgJUWFhYr7+wsFDR0dFuLeMvf/mLDhw4oMzMzDPWO336dEt1Aa3ahBU+LuBzH68fQFvXpCcUx8fHKy0tzRv1WPLMM8/ovvvu04033qjq6upGxyQnJyslJcX12el0Kj8/v7lKBAAAzcxyuKmurta6des8svIjR46orq5OoaGh9fpDQ0NVUFDwk/NOnDhRzz77rG666SZt3779jONqampUU1PjkXoBAPg5Ox7wdQVSpa8L8DHLFxTPnj1bTz75pEdWXltbqy1bttS7GNjhcCg2NlYbNmw443xPPfWUpkyZouHDh2vLli0eqQUAANiD5SM3AwcO1NChQ3X77bdrx44dDS4oHjFihKXlpaSkKC0tTZs3b9amTZs0fvx4dejQQampqZKktLQ05efna9KkSZKkp59+WklJSRo5cqT27t3rOupTXl6u48ePW/06AADAZiyHm+LiYn3wwQceK2DJkiUKCQlRUlKSwsLCtG3bNg0fPlyHDh2SJEVEROjkyZOu8Y899piCgoK0dOnSesuZPn26ZsyY4bG6AABA62Q53Jzp+TNnY+7cuZo7d26j04YMGVLvc8+ePT2+fgAAYB+Wr7kBAABoydw6cnP6ot/i4mL9+9///snn2fTv399jxQEAAFjlVrhZvny56zkyy5cv99h7pQAAADzNrXCTlJTk+jMX7f4Mng4LAIBPWb7mJicnR+eff36D/k6dOiknJ8cjRQEAADSV5XBz0UUXyd/fv0F/UFCQunfv7pGiAAAAmsrtW8H/67/+y/XnYcOGqaSkxPXZ399fsbGxys3N9Wx1AAAAFrkdbtLT0yWdevP3j1+aWVtbq71792rixIkeLQ4AAMAqt8PN6VNRe/bs0YABA3T06FGvFQUAaMW4sQI+ZvkJxRdffLE36gAAAPAIy+FGkoYOHarY2Fh17dpVfn71r0n+/e9/75HCAAAAmsJyuJk6daqmTp2qzZs36+DBgzzQDwAAtCiWw01CQoIefPBB/eMf//BGPQAAAGfF8nNuAgMDtX79em/UAgAAcNYsh5u///3vGjlypDdqAQAAOGuWT0udc845evTRR3XTTTfpq6++Um1tbb3pPOsGAAD4kuVwc+WVV2rbtm2SpCuuuKLeNC4uBgAAvmY53AwdOtQbdQAAAHiE5WtuAAAAWjK3j9wsXbrUrXEjRoxocjEAAABny+1w88O3gAMAALRUboeb+Ph4b9YBAADgEVxzAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbMXn4Wbs2LHKzc1VZWWlNm7cqAEDBpxx7GWXXab3339fubm5MsZo3LhxzVgpAABoDXwabuLi4pSSkqIZM2aoX79+ysrKUkZGhkJCQhod3759e+3Zs0fPPvusDh482MzVAgCA1sCn4WbChAlasGCBFi1apOzsbCUkJKiiokLx8fGNjt+8ebOefvppvffee6qurm7magEAQGvgs3DTrl079e/fX5mZma4+Y4wyMzMVExPjsfUEBgbK6XTWawAAwL58Fm66dOmigIAAFRYW1usvLCxUWFiYx9aTmJio0tJSV8vPz/fYsgEAQMvj8wuKvS05OVkdO3Z0tW7duvm6JAAA4EUBvlrxkSNHVFdXp9DQ0Hr9oaGhKigo8Nh6ampqVFNT47HlAQCAls1nR25qa2u1ZcsWxcbGuvocDodiY2O1YcMGX5UFAABaOZ8duZGklJQUpaWlafPmzdq0aZPGjx+vDh06KDU1VZKUlpam/Px8TZo0SdKpi5Avu+wySacuFO7WrZv69u2r8vJy5eTk+Ox7AACAlsOn4WbJkiUKCQlRUlKSwsLCtG3bNg0fPlyHDh2SJEVEROjkyZOu8eHh4dq2bZvr81NPPaWnnnpKa9as0ZAhQ5q7fAAA0AL5NNxI0ty5czV37txGp/04sOzbt08Oh6M5ygIAAK2U7e+WAgAAbQvhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2EqLCDdjx45Vbm6uKisrtXHjRg0YMOAnx99zzz3Kzs5WZWWlvvrqK91yyy3NVCkAAGjpfB5u4uLilJKSohkzZqhfv37KyspSRkaGQkJCGh0fExOjd999VwsXLtTVV1+t9PR0paen6/LLL2/mygEAQEvk83AzYcIELViwQIsWLVJ2drYSEhJUUVGh+Pj4RsePGzdOH3/8sWbNmqWdO3dq6tSp+ve//60nnniimSsHAAAtUYAvV96uXTv1799fycnJrj5jjDIzMxUTE9PoPDExMUpJSanXl5GRobvuuqvR8YGBgQoKCnJ9djqd9f7rcUE+/ZFK7YJ+fowX+Z3jpZ+ru+v39c9fXty23OXrnwHboE/XL7ENtvVtUPL9duiNbdDKMn367bt06aKAgAAVFhbW6y8sLFR0dHSj84SFhTU6PiwsrNHxiYmJmj59eoP+/Pz8phXd4vn4+qPRz/t2/S1A6bR7fF2Cj7EN+hrbINugr3lzG3Q6nSorK/vJMb7/J4aXJScnNzjSc/7556uoqMhHFeFMnE6n8vPz1a1bt5/dcAFvYBuEr7EN/jSn06kDBw787DifhpsjR46orq5OoaGh9fpDQ0NVUFDQ6DwFBQWWxtfU1KimpqZeHxtMy1ZWVsb/I/gU2yB8jW2wce7+THx6QXFtba22bNmi2NhYV5/D4VBsbKw2bNjQ6DwbNmyoN16Sbr755jOOBwAAbY/xZYuLizOVlZXmgQceMNHR0Wb+/PmmqKjIdO3a1UgyaWlp5oUXXnCNj4mJMTU1NWbChAkmKirKTJs2zVRXV5vLL7/cp9+DdvbN6XQaY4xxOp0+r4XWNhvbIM3XjW3QY83nBZjHH3/c7N2711RVVZmNGzeagQMHuqatXr3apKam1ht/zz33mJ07d5qqqiqzfft2c8stt/j8O9DOvgUGBppp06aZwMBAn9dCa5uNbZDm68Y26Jnm+M8fAAAAbMHnD/EDAADwJMINAACwFcINAACwFcINbMEYozvvvNPXZaCV6dGjh4wx6tu3r69LQRvFNugdhBt4lDHmJ9u0adPOOC87OZpbXl6ewsLC9PXXX5/Vcvr06aO1a9eqsrJS+/fv11NPPeWhCmF3ntgGg4KClJqaqq+++kq1tbVatmyZBytsnWz/+gU0rx++4+vee+9VUlKSoqKiXH3l5eW+KAtooF27dqqtrW3wrjqrnE6nVq1apczMTCUkJKhPnz568803VVxcrAULFnioWtiRp7ZBf39/VVZWas6cORoxYoSHqmv9fH4/Os2ebcyYMebYsWOuzw6Hw0yZMsXk5eWZqqoqs3XrVjNs2DDX9B9bvXq1kWSuueYas2rVKnP48GFTXFxs1qxZY66++up66zLGmDvvvNPn35nmm7Z69Wrz6quvmldffdUUFxebw4cPm6SkpHpjcnNzzeTJk01aWpopKSkxqamppkePHsYYY/r27esad/3115svvvjCVFVVmQMHDpjk5GTj7+9/xnUnJCSYo0ePmnbt2rn6kpOTTXZ2ts9/LrTma77cBn/YUlNTzbJly3z+82gBzecF0Gzafhxuxo8fb4qLi829995rLr30UvPiiy+a6upqExkZaaRTIcYYY4YOHWpCQ0PNeeedZySZIUOGmFGjRpmoqCgTHR1tFixYYA4ePGiCg4NdyybctO22evVqU1paav7617+aSy+91IwcOdKUl5ebhx9+2DUmNzfXFBcXmwkTJpiLL77YXHzxxQ1+sYSHh5vy8nLz2muvmaioKHPnnXeaQ4cOmWnTpp1x3WlpaQ1+mdx4443GGGM6d+7s858NrXmaL7fBHzbCjav5vACaTduPw833339vEhMT64354osvzGuvvWYkNfovmMaaw+EwJSUl5rbbbnP1EW7adlu9erXZsWNHvb7k5OR6fbm5ueaDDz6oN+bH29zMmTMbHHF57LHHTGlpqXE4HI2uOyMjw8yfP79eX+/evY0xxkRHR/v8Z0NrnubLbfCHjXBzqnFBMZqF0+lUt27dtG7dunr969atU+/evX9y3q5du+pvf/ubvv32WxUXF6u0tFTBwcGKiIjwZsloZTZu3Fjv84YNG3TJJZfIz+///zW3efPmn1xG7969G7yEd926dXI6nerevbvnioUtsQ22HFxQjBYvLS1Nv/jFLzRu3Djt27dP1dXV2rBhgwIDA31dGlqZ48ePe3yZBQUFCg0Nrdd3+nNBQYHH14fWzRvbIBriyA2aRVlZmfLz8zVo0KB6/YMGDdI333wjSaqpqZF06sr/H4+ZM2eO/vWvf+mbb75RdXW1QkJCmqdwtBrXXnttvc+//OUvtXv3bp08edLtZWRnZysmJqZe36BBg1RaWqrvv/++0Xk2bNig66+/XgEB///fijfffLN27typ4uJi978AWj1fbYNoiHCDZvPSSy/pmWeeUVxcnC699FIlJyfrqquu0uzZsyVJhw4dUkVFhYYPH66uXbuqY8eOkqTdu3frd7/7naKjozVw4EAtXrxYFRUVvvwqaIEiIiL08ssv69JLL9V9992nJ5980rVtuWvevHm68MIL9eqrryoqKkp33HGHZsyYoZSUFBljGp3nnXfeUU1NjRYuXKjLLrtMcXFxGjdunFJSUjzxtdCK+GoblE6dzurbt6/OP/98derUSX379m3zzwzz+YU/NHu2xm4Fnzp1qsnLyzPV1dUNbgWXZH7/+9+bffv2mbq6Otet4FdddZXZtGmTqaioMLt27TIjRowwubm5Zty4ca75uKC4bbfVq1eb1157zcybN88UFxebo0ePmpkzZ9Yb8+NtRmr8Ivam3Ibbp08fs3btWlNZWWny8vLM008/7fOfCa15m6+3wdzc3AaP0zCn0lCbbI7//AEAWq3Vq1dr27Zt+tOf/uTrUtBGsQ22LJyWAgAAtkK4AQAAtsJpKQAAYCscuQEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALby/wC+9raBNEjjggAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_cmp_fig([n_n_file, n_t_file, t_n_file, t_t_file], trace_file, labels=[\"QUIC\", \"DTP\", \"QUIC + DS\", \"DTP + DS\"], width=0.2, save=True, save_name=[\"1.svg\", \"2.svg\"], ylabel=['Intime Completion Rate', 'Average Latency'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.5 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b04cb35a734fdf897bd7e15000621a11839838d9d7b75235038ed64bbfdbae2d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
